Overview

Dataset statistics

Number of variables58
Number of observations27657
Missing cells406786
Missing cells (%)25.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.5 MiB
Average record size in memory436.0 B

Variable types

Numeric20
Text27
Categorical7
Boolean4

Alerts

status has constant value "act"Constant
live_viewing_url has constant value "https://my.matterport.com/show/?m=YRVdfY6Bvqa&ts=1"Constant
price_display_type is highly imbalanced (76.5%)Imbalance
price_unit is highly imbalanced (64.5%)Imbalance
is_furnished is highly imbalanced (50.2%)Imbalance
is_temporary is highly imbalanced (71.7%)Imbalance
is_selling_furniture is highly imbalanced (86.6%)Imbalance
reserved is highly imbalanced (99.7%)Imbalance
reference has 6129 (22.2%) missing valuesMissing
ref_property has 12672 (45.8%) missing valuesMissing
ref_house has 13995 (50.6%) missing valuesMissing
ref_object has 7885 (28.5%) missing valuesMissing
alternative_reference has 27231 (98.5%) missing valuesMissing
price_display has 1517 (5.5%) missing valuesMissing
price_display_type has 1517 (5.5%) missing valuesMissing
rent_net has 12433 (45.0%) missing valuesMissing
rent_charges has 13988 (50.6%) missing valuesMissing
rent_gross has 10400 (37.6%) missing valuesMissing
description_title has 691 (2.5%) missing valuesMissing
description has 943 (3.4%) missing valuesMissing
surface_living has 11073 (40.0%) missing valuesMissing
surface_property has 24232 (87.6%) missing valuesMissing
surface_usable has 19599 (70.9%) missing valuesMissing
surface_usable_minimum has 27319 (98.8%) missing valuesMissing
volume has 24061 (87.0%) missing valuesMissing
space_display has 5616 (20.3%) missing valuesMissing
number_of_rooms has 9351 (33.8%) missing valuesMissing
floor has 7885 (28.5%) missing valuesMissing
street has 4637 (16.8%) missing valuesMissing
year_built has 14588 (52.7%) missing valuesMissing
year_renovated has 22187 (80.2%) missing valuesMissing
moving_date has 19535 (70.6%) missing valuesMissing
video_url has 27219 (98.4%) missing valuesMissing
tour_url has 26071 (94.3%) missing valuesMissing
website_url has 20044 (72.5%) missing valuesMissing
live_viewing_url has 27654 (> 99.9%) missing valuesMissing
cover_image has 562 (2.0%) missing valuesMissing
livingspace has 5616 (20.3%) missing valuesMissing
surface_property is highly skewed (γ1 = 22.75337458)Skewed
surface_usable is highly skewed (γ1 = 21.03330719)Skewed
space_display is highly skewed (γ1 = 90.8017854)Skewed
year_built is highly skewed (γ1 = 114.2838607)Skewed
livingspace is highly skewed (γ1 = 90.8017854)Skewed
pk has unique valuesUnique
url has unique valuesUnique
short_url has unique valuesUnique
submit_url has unique valuesUnique
rent_charges has 1974 (7.1%) zerosZeros
rent_gross has 312 (1.1%) zerosZeros
surface_property has 651 (2.4%) zerosZeros
surface_usable has 1402 (5.1%) zerosZeros
volume has 2127 (7.7%) zerosZeros
space_display has 1235 (4.5%) zerosZeros
floor has 3501 (12.7%) zerosZeros
year_renovated has 285 (1.0%) zerosZeros
livingspace has 1235 (4.5%) zerosZeros

Reproduction

Analysis started2024-07-03 12:11:20.706358
Analysis finished2024-07-03 12:13:03.318850
Duration1 minute and 42.61 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

pk
Real number (ℝ)

UNIQUE 

Distinct27657
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1127864.7
Minimum26706
Maximum1254486
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.2 KiB
2024-07-03T14:13:04.154294image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum26706
5-th percentile729475.2
Q11104156
median1204456
Q31235959
95-th percentile1250801.4
Maximum1254486
Range1227780
Interquartile range (IQR)131803

Descriptive statistics

Standard deviation187244.52
Coefficient of variation (CV)0.16601682
Kurtosis8.6639084
Mean1127864.7
Median Absolute Deviation (MAD)39374
Skewness-2.7202551
Sum3.1193355 × 1010
Variance3.506051 × 1010
MonotonicityStrictly increasing
2024-07-03T14:13:04.420404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1254486 1
 
< 0.1%
26706 1
 
< 0.1%
33819 1
 
< 0.1%
44676 1
 
< 0.1%
46167 1
 
< 0.1%
51159 1
 
< 0.1%
56960 1
 
< 0.1%
58714 1
 
< 0.1%
58833 1
 
< 0.1%
1254315 1
 
< 0.1%
Other values (27647) 27647
> 99.9%
ValueCountFrequency (%)
26706 1
< 0.1%
33819 1
< 0.1%
44676 1
< 0.1%
46167 1
< 0.1%
51159 1
< 0.1%
56960 1
< 0.1%
58714 1
< 0.1%
58833 1
< 0.1%
58837 1
< 0.1%
62870 1
< 0.1%
ValueCountFrequency (%)
1254486 1
< 0.1%
1254483 1
< 0.1%
1254479 1
< 0.1%
1254477 1
< 0.1%
1254475 1
< 0.1%
1254474 1
< 0.1%
1254471 1
< 0.1%
1254468 1
< 0.1%
1254467 1
< 0.1%
1254463 1
< 0.1%

slug
Text

Distinct18709
Distinct (%)67.6%
Missing0
Missing (%)0.0%
Memory size216.2 KiB
2024-07-03T14:13:04.786404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length50
Median length42
Mean length27.734136
Min length7

Characters and Unicode

Total characters767043
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14746 ?
Unique (%)53.3%

Sample

1st rowparkstr-12-9430-st-margrethen-sg
2nd rowmartha-ringier-strasse-5600-lenzburg
3rd rowsonnentalstrasse-10-8600-dubendorf
4th rowwaldstrasse-5-9008-st-gallen
5th row8005-zurich
ValueCountFrequency (%)
1950-sion 93
 
0.3%
8004-zurich 56
 
0.2%
1958-uvrier 49
 
0.2%
1957-ardon 49
 
0.2%
3960-sierre 42
 
0.2%
8003-zurich 38
 
0.1%
8032-zurich 37
 
0.1%
9000-st-gallen 37
 
0.1%
1926-fully 33
 
0.1%
8050-zurich 33
 
0.1%
Other values (18699) 27190
98.3%
2024-07-03T14:13:05.418305image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 91864
 
12.0%
e 72081
 
9.4%
s 57554
 
7.5%
a 49713
 
6.5%
r 49268
 
6.4%
n 38424
 
5.0%
t 32202
 
4.2%
l 28755
 
3.7%
0 28127
 
3.7%
i 26777
 
3.5%
Other values (28) 292278
38.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 767043
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 91864
 
12.0%
e 72081
 
9.4%
s 57554
 
7.5%
a 49713
 
6.5%
r 49268
 
6.4%
n 38424
 
5.0%
t 32202
 
4.2%
l 28755
 
3.7%
0 28127
 
3.7%
i 26777
 
3.5%
Other values (28) 292278
38.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 767043
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 91864
 
12.0%
e 72081
 
9.4%
s 57554
 
7.5%
a 49713
 
6.5%
r 49268
 
6.4%
n 38424
 
5.0%
t 32202
 
4.2%
l 28755
 
3.7%
0 28127
 
3.7%
i 26777
 
3.5%
Other values (28) 292278
38.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 767043
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 91864
 
12.0%
e 72081
 
9.4%
s 57554
 
7.5%
a 49713
 
6.5%
r 49268
 
6.4%
n 38424
 
5.0%
t 32202
 
4.2%
l 28755
 
3.7%
0 28127
 
3.7%
i 26777
 
3.5%
Other values (28) 292278
38.1%

url
Text

UNIQUE 

Distinct27657
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size216.2 KiB
2024-07-03T14:13:05.867594image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length68
Median length60
Mean length45.583107
Min length25

Characters and Unicode

Total characters1260692
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27657 ?
Unique (%)100.0%

Sample

1st row/en/flat/parkstr-12-9430-st-margrethen-sg/26706/
2nd row/en/flat/martha-ringier-strasse-5600-lenzburg/33819/
3rd row/en/flat/sonnentalstrasse-10-8600-dubendorf/44676/
4th row/en/flat/waldstrasse-5-9008-st-gallen/46167/
5th row/en/flat/8005-zurich/51159/
ValueCountFrequency (%)
en/flat/hochstrasse-37-4053-basel/65630 1
 
< 0.1%
en/flat/bahnhofplatz-13-8953-dietikon/1254486 1
 
< 0.1%
en/flat/parkstr-12-9430-st-margrethen-sg/26706 1
 
< 0.1%
en/flat/martha-ringier-strasse-5600-lenzburg/33819 1
 
< 0.1%
en/flat/sonnentalstrasse-10-8600-dubendorf/44676 1
 
< 0.1%
en/flat/waldstrasse-5-9008-st-gallen/46167 1
 
< 0.1%
en/flat/8005-zurich/51159 1
 
< 0.1%
en/flat/loft-valangines-2000-neuchatel/56960 1
 
< 0.1%
en/flat/bahnhofstrasse-2527-8280-kreuzlingen/58714 1
 
< 0.1%
en/flat/schrennengasse-14-8003-zurich/1254374 1
 
< 0.1%
Other values (27647) 27647
> 99.9%
2024-07-03T14:13:06.615895image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 138285
 
11.0%
e 99738
 
7.9%
- 91864
 
7.3%
a 77370
 
6.1%
n 66081
 
5.2%
1 64005
 
5.1%
t 59859
 
4.7%
s 57554
 
4.6%
l 56412
 
4.5%
r 49268
 
3.9%
Other values (29) 500256
39.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1260692
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 138285
 
11.0%
e 99738
 
7.9%
- 91864
 
7.3%
a 77370
 
6.1%
n 66081
 
5.2%
1 64005
 
5.1%
t 59859
 
4.7%
s 57554
 
4.6%
l 56412
 
4.5%
r 49268
 
3.9%
Other values (29) 500256
39.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1260692
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 138285
 
11.0%
e 99738
 
7.9%
- 91864
 
7.3%
a 77370
 
6.1%
n 66081
 
5.2%
1 64005
 
5.1%
t 59859
 
4.7%
s 57554
 
4.6%
l 56412
 
4.5%
r 49268
 
3.9%
Other values (29) 500256
39.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1260692
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 138285
 
11.0%
e 99738
 
7.9%
- 91864
 
7.3%
a 77370
 
6.1%
n 66081
 
5.2%
1 64005
 
5.1%
t 59859
 
4.7%
s 57554
 
4.6%
l 56412
 
4.5%
r 49268
 
3.9%
Other values (29) 500256
39.7%

short_url
Text

UNIQUE 

Distinct27657
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size216.2 KiB
2024-07-03T14:13:07.248622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.8489713
Min length7

Characters and Unicode

Total characters244736
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27657 ?
Unique (%)100.0%

Sample

1st row/26706/
2nd row/33819/
3rd row/44676/
4th row/46167/
5th row/51159/
ValueCountFrequency (%)
65630 1
 
< 0.1%
1254486 1
 
< 0.1%
26706 1
 
< 0.1%
33819 1
 
< 0.1%
44676 1
 
< 0.1%
46167 1
 
< 0.1%
51159 1
 
< 0.1%
56960 1
 
< 0.1%
58714 1
 
< 0.1%
1254374 1
 
< 0.1%
Other values (27647) 27647
> 99.9%
2024-07-03T14:13:08.124408image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 55314
22.6%
1 43846
17.9%
2 28796
11.8%
0 16696
 
6.8%
4 16348
 
6.7%
3 15507
 
6.3%
9 14396
 
5.9%
8 14053
 
5.7%
5 13788
 
5.6%
7 13240
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 244736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 55314
22.6%
1 43846
17.9%
2 28796
11.8%
0 16696
 
6.8%
4 16348
 
6.7%
3 15507
 
6.3%
9 14396
 
5.9%
8 14053
 
5.7%
5 13788
 
5.6%
7 13240
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 244736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 55314
22.6%
1 43846
17.9%
2 28796
11.8%
0 16696
 
6.8%
4 16348
 
6.7%
3 15507
 
6.3%
9 14396
 
5.9%
8 14053
 
5.7%
5 13788
 
5.6%
7 13240
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 244736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 55314
22.6%
1 43846
17.9%
2 28796
11.8%
0 16696
 
6.8%
4 16348
 
6.7%
3 15507
 
6.3%
9 14396
 
5.9%
8 14053
 
5.7%
5 13788
 
5.6%
7 13240
 
5.4%

submit_url
Text

UNIQUE 

Distinct27657
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size216.2 KiB
2024-07-03T14:13:08.550958image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length27
Median length27
Mean length26.848971
Min length25

Characters and Unicode

Total characters742562
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27657 ?
Unique (%)100.0%

Sample

1st row/en/listing/26706/submit/
2nd row/en/listing/33819/submit/
3rd row/en/listing/44676/submit/
4th row/en/listing/46167/submit/
5th row/en/listing/51159/submit/
ValueCountFrequency (%)
en/listing/65630/submit 1
 
< 0.1%
en/listing/1254486/submit 1
 
< 0.1%
en/listing/26706/submit 1
 
< 0.1%
en/listing/33819/submit 1
 
< 0.1%
en/listing/44676/submit 1
 
< 0.1%
en/listing/46167/submit 1
 
< 0.1%
en/listing/51159/submit 1
 
< 0.1%
en/listing/56960/submit 1
 
< 0.1%
en/listing/58714/submit 1
 
< 0.1%
en/listing/1254374/submit 1
 
< 0.1%
Other values (27647) 27647
> 99.9%
2024-07-03T14:13:09.212917image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 138285
18.6%
i 82971
11.2%
n 55314
 
7.4%
t 55314
 
7.4%
s 55314
 
7.4%
1 43846
 
5.9%
2 28796
 
3.9%
l 27657
 
3.7%
e 27657
 
3.7%
g 27657
 
3.7%
Other values (11) 199751
26.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 742562
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 138285
18.6%
i 82971
11.2%
n 55314
 
7.4%
t 55314
 
7.4%
s 55314
 
7.4%
1 43846
 
5.9%
2 28796
 
3.9%
l 27657
 
3.7%
e 27657
 
3.7%
g 27657
 
3.7%
Other values (11) 199751
26.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 742562
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 138285
18.6%
i 82971
11.2%
n 55314
 
7.4%
t 55314
 
7.4%
s 55314
 
7.4%
1 43846
 
5.9%
2 28796
 
3.9%
l 27657
 
3.7%
e 27657
 
3.7%
g 27657
 
3.7%
Other values (11) 199751
26.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 742562
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 138285
18.6%
i 82971
11.2%
n 55314
 
7.4%
t 55314
 
7.4%
s 55314
 
7.4%
1 43846
 
5.9%
2 28796
 
3.9%
l 27657
 
3.7%
e 27657
 
3.7%
g 27657
 
3.7%
Other values (11) 199751
26.9%

status
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size216.2 KiB
act
27657 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters82971
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowact
2nd rowact
3rd rowact
4th rowact
5th rowact

Common Values

ValueCountFrequency (%)
act 27657
100.0%

Length

2024-07-03T14:13:09.453531image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-03T14:13:09.617188image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
act 27657
100.0%

Most occurring characters

ValueCountFrequency (%)
a 27657
33.3%
c 27657
33.3%
t 27657
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 82971
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 27657
33.3%
c 27657
33.3%
t 27657
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 82971
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 27657
33.3%
c 27657
33.3%
t 27657
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 82971
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 27657
33.3%
c 27657
33.3%
t 27657
33.3%

offer_type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size216.2 KiB
RENT
22072 
SALE
5585 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters110628
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRENT
2nd rowRENT
3rd rowRENT
4th rowRENT
5th rowRENT

Common Values

ValueCountFrequency (%)
RENT 22072
79.8%
SALE 5585
 
20.2%

Length

2024-07-03T14:13:09.783749image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-03T14:13:09.950653image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
rent 22072
79.8%
sale 5585
 
20.2%

Most occurring characters

ValueCountFrequency (%)
E 27657
25.0%
R 22072
20.0%
N 22072
20.0%
T 22072
20.0%
S 5585
 
5.0%
A 5585
 
5.0%
L 5585
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 110628
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 27657
25.0%
R 22072
20.0%
N 22072
20.0%
T 22072
20.0%
S 5585
 
5.0%
A 5585
 
5.0%
L 5585
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 110628
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 27657
25.0%
R 22072
20.0%
N 22072
20.0%
T 22072
20.0%
S 5585
 
5.0%
A 5585
 
5.0%
L 5585
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 110628
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 27657
25.0%
R 22072
20.0%
N 22072
20.0%
T 22072
20.0%
S 5585
 
5.0%
A 5585
 
5.0%
L 5585
 
5.0%

object_category
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size216.2 KiB
APARTMENT
14551 
INDUSTRY
4260 
PARK
4030 
HOUSE
2517 
SHARED
1678 
Other values (5)
 
621

Length

Max length11
Median length9
Mean length7.5427921
Min length4

Characters and Unicode

Total characters208611
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowINDUSTRY
2nd rowPARK
3rd rowINDUSTRY
4th rowAPARTMENT
5th rowSHARED

Common Values

ValueCountFrequency (%)
APARTMENT 14551
52.6%
INDUSTRY 4260
 
15.4%
PARK 4030
 
14.6%
HOUSE 2517
 
9.1%
SHARED 1678
 
6.1%
PROPERTY 225
 
0.8%
SECONDARY 206
 
0.7%
GASTRO 187
 
0.7%
GARDEN 2
 
< 0.1%
AGRICULTURE 1
 
< 0.1%

Length

2024-07-03T14:13:10.155346image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-03T14:13:10.390624image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
apartment 14551
52.6%
industry 4260
 
15.4%
park 4030
 
14.6%
house 2517
 
9.1%
shared 1678
 
6.1%
property 225
 
0.8%
secondary 206
 
0.7%
gastro 187
 
0.7%
garden 2
 
< 0.1%
agriculture 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 35206
16.9%
T 33775
16.2%
R 25366
12.2%
E 19180
9.2%
P 19031
9.1%
N 19019
9.1%
M 14551
7.0%
S 8848
 
4.2%
U 6779
 
3.2%
D 6146
 
2.9%
Other values (8) 20710
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 208611
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 35206
16.9%
T 33775
16.2%
R 25366
12.2%
E 19180
9.2%
P 19031
9.1%
N 19019
9.1%
M 14551
7.0%
S 8848
 
4.2%
U 6779
 
3.2%
D 6146
 
2.9%
Other values (8) 20710
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 208611
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 35206
16.9%
T 33775
16.2%
R 25366
12.2%
E 19180
9.2%
P 19031
9.1%
N 19019
9.1%
M 14551
7.0%
S 8848
 
4.2%
U 6779
 
3.2%
D 6146
 
2.9%
Other values (8) 20710
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 208611
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 35206
16.9%
T 33775
16.2%
R 25366
12.2%
E 19180
9.2%
P 19031
9.1%
N 19019
9.1%
M 14551
7.0%
S 8848
 
4.2%
U 6779
 
3.2%
D 6146
 
2.9%
Other values (8) 20710
9.9%
Distinct65
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size216.2 KiB
2024-07-03T14:13:10.774013image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length26
Median length18
Mean length9.8609755
Min length3

Characters and Unicode

Total characters272725
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowOFFICE
2nd rowGARAGE_SLOT
3rd rowATELIER
4th rowSINGLE_ROOM
5th rowSHARED_FLAT
ValueCountFrequency (%)
apartment 11922
43.1%
garage_slot 2409
 
8.7%
office 2091
 
7.6%
shared_flat 1678
 
6.1%
single_house 1392
 
5.0%
furnished_flat 1139
 
4.1%
open_slot 1056
 
3.8%
commercial 751
 
2.7%
storage_room 489
 
1.8%
shop 484
 
1.8%
Other values (55) 4246
 
15.4%
2024-07-03T14:13:11.367051image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 38633
14.2%
T 33864
12.4%
E 28881
10.6%
R 21765
 
8.0%
N 17046
 
6.3%
P 15165
 
5.6%
M 15023
 
5.5%
O 14031
 
5.1%
L 12452
 
4.6%
_ 12270
 
4.5%
Other values (15) 63595
23.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 272725
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 38633
14.2%
T 33864
12.4%
E 28881
10.6%
R 21765
 
8.0%
N 17046
 
6.3%
P 15165
 
5.6%
M 15023
 
5.5%
O 14031
 
5.1%
L 12452
 
4.6%
_ 12270
 
4.5%
Other values (15) 63595
23.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 272725
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 38633
14.2%
T 33864
12.4%
E 28881
10.6%
R 21765
 
8.0%
N 17046
 
6.3%
P 15165
 
5.6%
M 15023
 
5.5%
O 14031
 
5.1%
L 12452
 
4.6%
_ 12270
 
4.5%
Other values (15) 63595
23.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 272725
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 38633
14.2%
T 33864
12.4%
E 28881
10.6%
R 21765
 
8.0%
N 17046
 
6.3%
P 15165
 
5.6%
M 15023
 
5.5%
O 14031
 
5.1%
L 12452
 
4.6%
_ 12270
 
4.5%
Other values (15) 63595
23.3%

reference
Text

MISSING 

Distinct21528
Distinct (%)100.0%
Missing6129
Missing (%)22.2%
Memory size216.2 KiB
2024-07-03T14:13:11.821735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length116
Median length99
Mean length19.717066
Min length3

Characters and Unicode

Total characters424469
Distinct characters89
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21528 ?
Unique (%)100.0%

Sample

1st row904250.01.40102
2nd row194..0
3rd row71.10 (L 00 02).9001
4th rowBahnhofstrasse..Garage.308168.b2ac31f4-d78d-11e8-bb6c-a4bf01195aaa
5th rowKirchweg..PPL.308154.abdd07cf-d78d-11e8-bb6c-a4bf01195aaa
ValueCountFrequency (%)
apartment 185
 
0.8%
131
 
0.5%
bedroom 79
 
0.3%
junior 74
 
0.3%
senior 52
 
0.2%
de 46
 
0.2%
im 43
 
0.2%
mini 38
 
0.2%
we 35
 
0.1%
og 34
 
0.1%
Other values (22155) 23428
97.0%
2024-07-03T14:13:12.562609image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 64718
15.2%
1 46312
 
10.9%
. 44990
 
10.6%
2 28374
 
6.7%
4 22669
 
5.3%
3 21920
 
5.2%
6 19601
 
4.6%
e 16816
 
4.0%
- 16546
 
3.9%
8 16195
 
3.8%
Other values (79) 126328
29.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 424469
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 64718
15.2%
1 46312
 
10.9%
. 44990
 
10.6%
2 28374
 
6.7%
4 22669
 
5.3%
3 21920
 
5.2%
6 19601
 
4.6%
e 16816
 
4.0%
- 16546
 
3.9%
8 16195
 
3.8%
Other values (79) 126328
29.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 424469
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 64718
15.2%
1 46312
 
10.9%
. 44990
 
10.6%
2 28374
 
6.7%
4 22669
 
5.3%
3 21920
 
5.2%
6 19601
 
4.6%
e 16816
 
4.0%
- 16546
 
3.9%
8 16195
 
3.8%
Other values (79) 126328
29.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 424469
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 64718
15.2%
1 46312
 
10.9%
. 44990
 
10.6%
2 28374
 
6.7%
4 22669
 
5.3%
3 21920
 
5.2%
6 19601
 
4.6%
e 16816
 
4.0%
- 16546
 
3.9%
8 16195
 
3.8%
Other values (79) 126328
29.8%

ref_property
Text

MISSING 

Distinct9173
Distinct (%)61.2%
Missing12672
Missing (%)45.8%
Memory size216.2 KiB
2024-07-03T14:13:13.200768image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length61
Median length58
Mean length6.2908909
Min length1

Characters and Unicode

Total characters94269
Distinct characters82
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6746 ?
Unique (%)45.0%

Sample

1st row904250
2nd row194
3rd row71
4th rowBahnhofstrasse..Garage
5th rowKirchweg..PPL
ValueCountFrequency (%)
01 538
 
3.4%
1 115
 
0.7%
zurich 112
 
0.7%
105
 
0.7%
vm100 73
 
0.5%
we 35
 
0.2%
lr_jgab 28
 
0.2%
2 22
 
0.1%
le 22
 
0.1%
3 22
 
0.1%
Other values (9379) 14885
93.3%
2024-07-03T14:13:14.142948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 14166
15.0%
1 11571
12.3%
2 8970
 
9.5%
4 6261
 
6.6%
3 6215
 
6.6%
5 5433
 
5.8%
8 4691
 
5.0%
6 4684
 
5.0%
7 4401
 
4.7%
9 3751
 
4.0%
Other values (72) 24126
25.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 94269
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 14166
15.0%
1 11571
12.3%
2 8970
 
9.5%
4 6261
 
6.6%
3 6215
 
6.6%
5 5433
 
5.8%
8 4691
 
5.0%
6 4684
 
5.0%
7 4401
 
4.7%
9 3751
 
4.0%
Other values (72) 24126
25.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 94269
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 14166
15.0%
1 11571
12.3%
2 8970
 
9.5%
4 6261
 
6.6%
3 6215
 
6.6%
5 5433
 
5.8%
8 4691
 
5.0%
6 4684
 
5.0%
7 4401
 
4.7%
9 3751
 
4.0%
Other values (72) 24126
25.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 94269
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 14166
15.0%
1 11571
12.3%
2 8970
 
9.5%
4 6261
 
6.6%
3 6215
 
6.6%
5 5433
 
5.8%
8 4691
 
5.0%
6 4684
 
5.0%
7 4401
 
4.7%
9 3751
 
4.0%
Other values (72) 24126
25.6%

ref_house
Text

MISSING 

Distinct4300
Distinct (%)31.5%
Missing13995
Missing (%)50.6%
Memory size216.2 KiB
2024-07-03T14:13:14.648809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length71
Median length2
Mean length3.9509589
Min length1

Characters and Unicode

Total characters53978
Distinct characters83
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3940 ?
Unique (%)28.8%

Sample

1st row01
2nd row10 (L 00 02)
3rd row308168
4th row308154
5th row308164
ValueCountFrequency (%)
01 2846
 
19.8%
02 1090
 
7.6%
03 588
 
4.1%
1 460
 
3.2%
70 447
 
3.1%
04 364
 
2.5%
80 255
 
1.8%
05 243
 
1.7%
90 215
 
1.5%
06 172
 
1.2%
Other values (4391) 7681
53.5%
2024-07-03T14:13:15.462630image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11072
20.5%
1 9821
18.2%
3 4281
 
7.9%
4 4273
 
7.9%
2 4217
 
7.8%
9 2558
 
4.7%
7 2459
 
4.6%
8 2346
 
4.3%
5 2285
 
4.2%
6 2244
 
4.2%
Other values (73) 8422
15.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 53978
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11072
20.5%
1 9821
18.2%
3 4281
 
7.9%
4 4273
 
7.9%
2 4217
 
7.8%
9 2558
 
4.7%
7 2459
 
4.6%
8 2346
 
4.3%
5 2285
 
4.2%
6 2244
 
4.2%
Other values (73) 8422
15.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 53978
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11072
20.5%
1 9821
18.2%
3 4281
 
7.9%
4 4273
 
7.9%
2 4217
 
7.8%
9 2558
 
4.7%
7 2459
 
4.6%
8 2346
 
4.3%
5 2285
 
4.2%
6 2244
 
4.2%
Other values (73) 8422
15.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 53978
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11072
20.5%
1 9821
18.2%
3 4281
 
7.9%
4 4273
 
7.9%
2 4217
 
7.8%
9 2558
 
4.7%
7 2459
 
4.6%
8 2346
 
4.3%
5 2285
 
4.2%
6 2244
 
4.2%
Other values (73) 8422
15.6%

ref_object
Text

MISSING 

Distinct12348
Distinct (%)62.5%
Missing7885
Missing (%)28.5%
Memory size216.2 KiB
2024-07-03T14:13:16.153541image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length53
Median length51
Mean length11.792737
Min length1

Characters and Unicode

Total characters233166
Distinct characters79
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11092 ?
Unique (%)56.1%

Sample

1st row40102
2nd row0
3rd row9001
4th rowb2ac31f4-d78d-11e8-bb6c-a4bf01195aaa
5th rowabdd07cf-d78d-11e8-bb6c-a4bf01195aaa
ValueCountFrequency (%)
0001 203
 
1.0%
0201 192
 
0.9%
apartment 187
 
0.9%
0101 163
 
0.8%
0002 150
 
0.7%
0301 135
 
0.7%
0102 130
 
0.6%
0202 123
 
0.6%
0003 121
 
0.6%
studio 101
 
0.5%
Other values (12274) 19209
92.7%
2024-07-03T14:13:17.143249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 39480
16.9%
1 24920
 
10.7%
2 15187
 
6.5%
e 13987
 
6.0%
- 13915
 
6.0%
6 12673
 
5.4%
4 12135
 
5.2%
a 11728
 
5.0%
3 11424
 
4.9%
8 9158
 
3.9%
Other values (69) 68559
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 233166
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 39480
16.9%
1 24920
 
10.7%
2 15187
 
6.5%
e 13987
 
6.0%
- 13915
 
6.0%
6 12673
 
5.4%
4 12135
 
5.2%
a 11728
 
5.0%
3 11424
 
4.9%
8 9158
 
3.9%
Other values (69) 68559
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 233166
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 39480
16.9%
1 24920
 
10.7%
2 15187
 
6.5%
e 13987
 
6.0%
- 13915
 
6.0%
6 12673
 
5.4%
4 12135
 
5.2%
a 11728
 
5.0%
3 11424
 
4.9%
8 9158
 
3.9%
Other values (69) 68559
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 233166
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 39480
16.9%
1 24920
 
10.7%
2 15187
 
6.5%
e 13987
 
6.0%
- 13915
 
6.0%
6 12673
 
5.4%
4 12135
 
5.2%
a 11728
 
5.0%
3 11424
 
4.9%
8 9158
 
3.9%
Other values (69) 68559
29.4%

alternative_reference
Text

MISSING 

Distinct395
Distinct (%)92.7%
Missing27231
Missing (%)98.5%
Memory size216.2 KiB
2024-07-03T14:13:17.602677image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length113
Median length64
Mean length20.326291
Min length1

Characters and Unicode

Total characters8659
Distinct characters89
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique375 ?
Unique (%)88.0%

Sample

1st rowGrand Apartment Terrace
2nd row1 Bedroom Apartment Senior Terrace
3rd row2 Bedroom Apartment Junior Terrace
4th row2 Bedroom Apartment Junior Balcony
5th row2 Bedroom Apartment Senior Terrace
ValueCountFrequency (%)
40
 
3.2%
og 29
 
2.3%
im 28
 
2.2%
de 27
 
2.1%
nr 24
 
1.9%
2 23
 
1.8%
zimmerwohnung 14
 
1.1%
1 12
 
1.0%
büro 11
 
0.9%
apartment 11
 
0.9%
Other values (684) 1038
82.6%
2024-07-03T14:13:18.326294image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
833
 
9.6%
e 693
 
8.0%
r 450
 
5.2%
n 394
 
4.6%
0 373
 
4.3%
t 357
 
4.1%
i 348
 
4.0%
a 344
 
4.0%
s 294
 
3.4%
1 268
 
3.1%
Other values (79) 4305
49.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8659
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
833
 
9.6%
e 693
 
8.0%
r 450
 
5.2%
n 394
 
4.6%
0 373
 
4.3%
t 357
 
4.1%
i 348
 
4.0%
a 344
 
4.0%
s 294
 
3.4%
1 268
 
3.1%
Other values (79) 4305
49.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8659
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
833
 
9.6%
e 693
 
8.0%
r 450
 
5.2%
n 394
 
4.6%
0 373
 
4.3%
t 357
 
4.1%
i 348
 
4.0%
a 344
 
4.0%
s 294
 
3.4%
1 268
 
3.1%
Other values (79) 4305
49.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8659
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
833
 
9.6%
e 693
 
8.0%
r 450
 
5.2%
n 394
 
4.6%
0 373
 
4.3%
t 357
 
4.1%
i 348
 
4.0%
a 344
 
4.0%
s 294
 
3.4%
1 268
 
3.1%
Other values (79) 4305
49.7%

price_display
Real number (ℝ)

MISSING 

Distinct3723
Distinct (%)14.2%
Missing1517
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean203911.78
Minimum1
Maximum28000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.2 KiB
2024-07-03T14:13:18.578030image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile100
Q1800
median1750
Q33448.5
95-th percentile1245000
Maximum28000000
Range27999999
Interquartile range (IQR)2648.5

Descriptive statistics

Standard deviation622819.09
Coefficient of variation (CV)3.0543556
Kurtosis270.01526
Mean203911.78
Median Absolute Deviation (MAD)1193
Skewness10.592031
Sum5.330254 × 109
Variance3.8790362 × 1011
MonotonicityNot monotonic
2024-07-03T14:13:18.822974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 453
 
1.6%
130 381
 
1.4%
150 367
 
1.3%
100 256
 
0.9%
50 220
 
0.8%
140 218
 
0.8%
110 187
 
0.7%
1500 170
 
0.6%
160 140
 
0.5%
250 140
 
0.5%
Other values (3713) 23608
85.4%
(Missing) 1517
 
5.5%
ValueCountFrequency (%)
1 11
 
< 0.1%
10 9
 
< 0.1%
15 6
 
< 0.1%
20 58
0.2%
21 1
 
< 0.1%
22 1
 
< 0.1%
25 32
0.1%
26 1
 
< 0.1%
27 1
 
< 0.1%
28 1
 
< 0.1%
ValueCountFrequency (%)
28000000 1
< 0.1%
19000000 1
< 0.1%
16300000 1
< 0.1%
15000000 2
< 0.1%
12900000 1
< 0.1%
12500000 1
< 0.1%
12000000 1
< 0.1%
10900000 1
< 0.1%
10600000 1
< 0.1%
9564000 1
< 0.1%

price_display_type
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing1517
Missing (%)5.5%
Memory size216.2 KiB
TOTAL
25133 
M2
 
1007

Length

Max length5
Median length5
Mean length4.88443
Min length2

Characters and Unicode

Total characters127679
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTOTAL
2nd rowTOTAL
3rd rowTOTAL
4th rowTOTAL
5th rowTOTAL

Common Values

ValueCountFrequency (%)
TOTAL 25133
90.9%
M2 1007
 
3.6%
(Missing) 1517
 
5.5%

Length

2024-07-03T14:13:19.058499image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-03T14:13:19.232039image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
total 25133
96.1%
m2 1007
 
3.9%

Most occurring characters

ValueCountFrequency (%)
T 50266
39.4%
O 25133
19.7%
A 25133
19.7%
L 25133
19.7%
M 1007
 
0.8%
2 1007
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 127679
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 50266
39.4%
O 25133
19.7%
A 25133
19.7%
L 25133
19.7%
M 1007
 
0.8%
2 1007
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 127679
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 50266
39.4%
O 25133
19.7%
A 25133
19.7%
L 25133
19.7%
M 1007
 
0.8%
2 1007
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 127679
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 50266
39.4%
O 25133
19.7%
A 25133
19.7%
L 25133
19.7%
M 1007
 
0.8%
2 1007
 
0.8%

price_unit
Categorical

IMBALANCE 

Distinct7
Distinct (%)< 0.1%
Missing134
Missing (%)0.5%
Memory size216.2 KiB
monthly
20716 
sell
5501 
yearlym2
 
1246
sellm2
 
43
yearly
 
9
Other values (2)
 
8

Length

Max length8
Median length7
Mean length6.4432293
Min length4

Characters and Unicode

Total characters177337
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowmonthly
2nd rowmonthly
3rd rowmonthly
4th rowmonthly
5th rowmonthly

Common Values

ValueCountFrequency (%)
monthly 20716
74.9%
sell 5501
 
19.9%
yearlym2 1246
 
4.5%
sellm2 43
 
0.2%
yearly 9
 
< 0.1%
daily 7
 
< 0.1%
weekly 1
 
< 0.1%
(Missing) 134
 
0.5%

Length

2024-07-03T14:13:19.435649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-03T14:13:19.650412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
monthly 20716
75.3%
sell 5501
 
20.0%
yearlym2 1246
 
4.5%
sellm2 43
 
0.2%
yearly 9
 
< 0.1%
daily 7
 
< 0.1%
weekly 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
l 33067
18.6%
y 23234
13.1%
m 22005
12.4%
o 20716
11.7%
t 20716
11.7%
n 20716
11.7%
h 20716
11.7%
e 6801
 
3.8%
s 5544
 
3.1%
2 1289
 
0.7%
Other values (6) 2533
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 177337
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 33067
18.6%
y 23234
13.1%
m 22005
12.4%
o 20716
11.7%
t 20716
11.7%
n 20716
11.7%
h 20716
11.7%
e 6801
 
3.8%
s 5544
 
3.1%
2 1289
 
0.7%
Other values (6) 2533
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 177337
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 33067
18.6%
y 23234
13.1%
m 22005
12.4%
o 20716
11.7%
t 20716
11.7%
n 20716
11.7%
h 20716
11.7%
e 6801
 
3.8%
s 5544
 
3.1%
2 1289
 
0.7%
Other values (6) 2533
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 177337
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 33067
18.6%
y 23234
13.1%
m 22005
12.4%
o 20716
11.7%
t 20716
11.7%
n 20716
11.7%
h 20716
11.7%
e 6801
 
3.8%
s 5544
 
3.1%
2 1289
 
0.7%
Other values (6) 2533
 
1.4%

rent_net
Real number (ℝ)

MISSING 

Distinct2305
Distinct (%)15.1%
Missing12433
Missing (%)45.0%
Infinite0
Infinite (%)0.0%
Mean1500.5416
Minimum0
Maximum56786
Zeros22
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size216.2 KiB
2024-07-03T14:13:19.881066image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile80
Q1290
median1330
Q31970
95-th percentile3793.4
Maximum56786
Range56786
Interquartile range (IQR)1680

Descriptive statistics

Standard deviation1592.1356
Coefficient of variation (CV)1.0610406
Kurtosis175.16376
Mean1500.5416
Median Absolute Deviation (MAD)770
Skewness7.596263
Sum22844246
Variance2534895.7
MonotonicityNot monotonic
2024-07-03T14:13:20.125252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 353
 
1.3%
130 306
 
1.1%
150 268
 
1.0%
100 190
 
0.7%
140 171
 
0.6%
1200 153
 
0.6%
1300 148
 
0.5%
50 145
 
0.5%
110 144
 
0.5%
1500 142
 
0.5%
Other values (2295) 13204
47.7%
(Missing) 12433
45.0%
ValueCountFrequency (%)
0 22
0.1%
1 1
 
< 0.1%
10 8
 
< 0.1%
13 1
 
< 0.1%
15 5
 
< 0.1%
18 2
 
< 0.1%
20 53
0.2%
25 29
0.1%
26 1
 
< 0.1%
27 1
 
< 0.1%
ValueCountFrequency (%)
56786 1
< 0.1%
49450 1
< 0.1%
30000 1
< 0.1%
27155 1
< 0.1%
22000 1
< 0.1%
20105 1
< 0.1%
20000 1
< 0.1%
19390 1
< 0.1%
18493 1
< 0.1%
18000 1
< 0.1%

rent_charges
Real number (ℝ)

MISSING  ZEROS 

Distinct508
Distinct (%)3.7%
Missing13988
Missing (%)50.6%
Infinite0
Infinite (%)0.0%
Mean197.26293
Minimum0
Maximum10750
Zeros1974
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size216.2 KiB
2024-07-03T14:13:20.383489image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q195
median200
Q3270
95-th percentile420
Maximum10750
Range10750
Interquartile range (IQR)175

Descriptive statistics

Standard deviation197.69174
Coefficient of variation (CV)1.0021738
Kurtosis666.75403
Mean197.26293
Median Absolute Deviation (MAD)83
Skewness15.423192
Sum2696387
Variance39082.023
MonotonicityNot monotonic
2024-07-03T14:13:20.640159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1974
 
7.1%
200 1070
 
3.9%
250 854
 
3.1%
150 672
 
2.4%
300 635
 
2.3%
220 387
 
1.4%
180 365
 
1.3%
100 336
 
1.2%
240 312
 
1.1%
230 303
 
1.1%
Other values (498) 6761
24.4%
(Missing) 13988
50.6%
ValueCountFrequency (%)
0 1974
7.1%
1 10
 
< 0.1%
2 7
 
< 0.1%
3 5
 
< 0.1%
4 3
 
< 0.1%
5 83
 
0.3%
6 1
 
< 0.1%
7 11
 
< 0.1%
8 7
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
10750 1
< 0.1%
5753 1
< 0.1%
3900 1
< 0.1%
3010 1
< 0.1%
3000 1
< 0.1%
2560 1
< 0.1%
2500 1
< 0.1%
2494 1
< 0.1%
2400 1
< 0.1%
2378 1
< 0.1%

rent_gross
Real number (ℝ)

MISSING  ZEROS 

Distinct2418
Distinct (%)14.0%
Missing10400
Missing (%)37.6%
Infinite0
Infinite (%)0.0%
Mean1483.7558
Minimum0
Maximum62539
Zeros312
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size216.2 KiB
2024-07-03T14:13:20.882852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q1250
median1410
Q32050
95-th percentile3570
Maximum62539
Range62539
Interquartile range (IQR)1800

Descriptive statistics

Standard deviation1463.4517
Coefficient of variation (CV)0.98631576
Kurtosis200.73715
Mean1483.7558
Median Absolute Deviation (MAD)810
Skewness7.044555
Sum25605174
Variance2141691
MonotonicityNot monotonic
2024-07-03T14:13:21.128984image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 405
 
1.5%
130 346
 
1.3%
150 329
 
1.2%
0 312
 
1.1%
100 230
 
0.8%
50 197
 
0.7%
140 194
 
0.7%
110 175
 
0.6%
1500 143
 
0.5%
60 131
 
0.5%
Other values (2408) 14795
53.5%
(Missing) 10400
37.6%
ValueCountFrequency (%)
0 312
1.1%
1 6
 
< 0.1%
10 9
 
< 0.1%
15 5
 
< 0.1%
20 56
 
0.2%
22 1
 
< 0.1%
25 29
 
0.1%
26 1
 
< 0.1%
27 1
 
< 0.1%
28 1
 
< 0.1%
ValueCountFrequency (%)
62539 1
< 0.1%
30000 1
< 0.1%
25000 1
< 0.1%
20500 1
< 0.1%
20000 1
< 0.1%
17520 1
< 0.1%
17302 1
< 0.1%
17114 1
< 0.1%
17035 1
< 0.1%
15000 2
< 0.1%
Distinct2343
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size216.2 KiB
2024-07-03T14:13:21.785086image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length35
Median length33
Mean length17.407456
Min length4

Characters and Unicode

Total characters481438
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1373 ?
Unique (%)5.0%

Sample

1st row127m² office
2nd rowUnderground slot
3rd row119m² atelier
4th row1 room single room
5th rowRoom in a shared flat
ValueCountFrequency (%)
rooms 15832
16.7%
½ 12418
13.1%
apartment 11922
12.6%
3 5045
 
5.3%
4 4041
 
4.3%
flat 3595
 
3.8%
slot 3485
 
3.7%
2 3171
 
3.3%
room 3167
 
3.3%
underground 2409
 
2.5%
Other values (1076) 29572
31.2%
2024-07-03T14:13:22.711397image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
67000
13.9%
o 51704
10.7%
r 40781
 
8.5%
m 37567
 
7.8%
a 34848
 
7.2%
t 33846
 
7.0%
e 28458
 
5.9%
s 27045
 
5.6%
n 23556
 
4.9%
p 14702
 
3.1%
Other values (46) 121931
25.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 481438
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
67000
13.9%
o 51704
10.7%
r 40781
 
8.5%
m 37567
 
7.8%
a 34848
 
7.2%
t 33846
 
7.0%
e 28458
 
5.9%
s 27045
 
5.6%
n 23556
 
4.9%
p 14702
 
3.1%
Other values (46) 121931
25.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 481438
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
67000
13.9%
o 51704
10.7%
r 40781
 
8.5%
m 37567
 
7.8%
a 34848
 
7.2%
t 33846
 
7.0%
e 28458
 
5.9%
s 27045
 
5.6%
n 23556
 
4.9%
p 14702
 
3.1%
Other values (46) 121931
25.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 481438
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
67000
13.9%
o 51704
10.7%
r 40781
 
8.5%
m 37567
 
7.8%
a 34848
 
7.2%
t 33846
 
7.0%
e 28458
 
5.9%
s 27045
 
5.6%
n 23556
 
4.9%
p 14702
 
3.1%
Other values (46) 121931
25.3%
Distinct26217
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Memory size216.2 KiB
2024-07-03T14:13:23.403810image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length118
Median length95
Mean length60.729219
Min length19

Characters and Unicode

Total characters1679588
Distinct characters119
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25182 ?
Unique (%)91.1%

Sample

1st rowParkstr. 12, 9430 St. Margrethen SG - by request
2nd rowMartha Ringier-Strasse, 5600 Lenzburg - CHF 130 excl. utilities per month
3rd rowSonnentalstrasse 10, 8600 Dübendorf - CHF 2’325 incl. utilities per month
4th rowWaldstrasse 5, 9008 St. Gallen - CHF 610 incl. utilities per month
5th row8005 Zürich - CHF 1’350 incl. utilities per month
ValueCountFrequency (%)
29155
 
10.2%
chf 26140
 
9.2%
utilities 21040
 
7.4%
per 21040
 
7.4%
month 20061
 
7.1%
incl 19648
 
6.9%
zürich 2145
 
0.8%
request 1517
 
0.5%
by 1517
 
0.5%
de 1478
 
0.5%
Other values (17481) 140772
49.5%
2024-07-03T14:13:24.385728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
256909
 
15.3%
e 115684
 
6.9%
i 108662
 
6.5%
t 94503
 
5.6%
n 76837
 
4.6%
s 72770
 
4.3%
0 68003
 
4.0%
r 67972
 
4.0%
l 66476
 
4.0%
a 42363
 
2.5%
Other values (109) 709409
42.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1679588
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
256909
 
15.3%
e 115684
 
6.9%
i 108662
 
6.5%
t 94503
 
5.6%
n 76837
 
4.6%
s 72770
 
4.3%
0 68003
 
4.0%
r 67972
 
4.0%
l 66476
 
4.0%
a 42363
 
2.5%
Other values (109) 709409
42.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1679588
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
256909
 
15.3%
e 115684
 
6.9%
i 108662
 
6.5%
t 94503
 
5.6%
n 76837
 
4.6%
s 72770
 
4.3%
0 68003
 
4.0%
r 67972
 
4.0%
l 66476
 
4.0%
a 42363
 
2.5%
Other values (109) 709409
42.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1679588
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
256909
 
15.3%
e 115684
 
6.9%
i 108662
 
6.5%
t 94503
 
5.6%
n 76837
 
4.6%
s 72770
 
4.3%
0 68003
 
4.0%
r 67972
 
4.0%
l 66476
 
4.0%
a 42363
 
2.5%
Other values (109) 709409
42.2%
Distinct15298
Distinct (%)55.3%
Missing0
Missing (%)0.0%
Memory size216.2 KiB
2024-07-03T14:13:24.912514image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length90
Median length59
Mean length36.500597
Min length17

Characters and Unicode

Total characters1009497
Distinct characters102
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12180 ?
Unique (%)44.0%

Sample

1st rowRent a 127m² office in St. Margrethen SG
2nd rowRent a underground slot in Lenzburg
3rd rowRent a 119m² atelier in Dübendorf
4th rowRent a 1 room single room in St. Gallen
5th rowRent a room in a shared flat in Zürich
ValueCountFrequency (%)
a 29338
14.0%
in 29335
14.0%
rent 22072
 
10.5%
rooms 15832
 
7.6%
½ 12418
 
5.9%
apartment 11922
 
5.7%
buy 5585
 
2.7%
3 5045
 
2.4%
4 4041
 
1.9%
flat 3595
 
1.7%
Other values (3691) 70353
33.6%
2024-07-03T14:13:25.765030image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
181881
18.0%
n 94338
 
9.3%
a 76341
 
7.6%
e 75573
 
7.5%
t 65118
 
6.5%
o 61269
 
6.1%
r 58575
 
5.8%
i 53119
 
5.3%
m 39627
 
3.9%
s 37190
 
3.7%
Other values (92) 266466
26.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1009497
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
181881
18.0%
n 94338
 
9.3%
a 76341
 
7.6%
e 75573
 
7.5%
t 65118
 
6.5%
o 61269
 
6.1%
r 58575
 
5.8%
i 53119
 
5.3%
m 39627
 
3.9%
s 37190
 
3.7%
Other values (92) 266466
26.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1009497
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
181881
18.0%
n 94338
 
9.3%
a 76341
 
7.6%
e 75573
 
7.5%
t 65118
 
6.5%
o 61269
 
6.1%
r 58575
 
5.8%
i 53119
 
5.3%
m 39627
 
3.9%
s 37190
 
3.7%
Other values (92) 266466
26.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1009497
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
181881
18.0%
n 94338
 
9.3%
a 76341
 
7.6%
e 75573
 
7.5%
t 65118
 
6.5%
o 61269
 
6.1%
r 58575
 
5.8%
i 53119
 
5.3%
m 39627
 
3.9%
s 37190
 
3.7%
Other values (92) 266466
26.4%

description_title
Text

MISSING 

Distinct23385
Distinct (%)86.7%
Missing691
Missing (%)2.5%
Memory size216.2 KiB
2024-07-03T14:13:26.208528image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length314
Median length159
Mean length43.242565
Min length1

Characters and Unicode

Total characters1166079
Distinct characters176
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21741 ?
Unique (%)80.6%

Sample

1st rowRepräsentativer Büro- oder Praxisraum von ca. 127m2
2nd rowPARKPLATZKONTINGENT IN LENZBURG
3rd rowHochwertiges Atelier Hochbord Dübendorf
4th rowMöbiliertes WG Zimmer zum fairen Preis
5th rowFurnished room with morning sun
ValueCountFrequency (%)
in 6496
 
3.9%
4964
 
3.0%
mit 3677
 
2.2%
de 3173
 
1.9%
im 2567
 
1.6%
zu 2410
 
1.5%
vermieten 2213
 
1.3%
und 2139
 
1.3%
wohnung 1960
 
1.2%
an 1783
 
1.1%
Other values (14112) 133421
81.0%
2024-07-03T14:13:26.919879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
138176
 
11.8%
e 116696
 
10.0%
n 74263
 
6.4%
i 64272
 
5.5%
r 61609
 
5.3%
t 53417
 
4.6%
a 53150
 
4.6%
u 38357
 
3.3%
l 36797
 
3.2%
m 36248
 
3.1%
Other values (166) 493094
42.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1166079
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
138176
 
11.8%
e 116696
 
10.0%
n 74263
 
6.4%
i 64272
 
5.5%
r 61609
 
5.3%
t 53417
 
4.6%
a 53150
 
4.6%
u 38357
 
3.3%
l 36797
 
3.2%
m 36248
 
3.1%
Other values (166) 493094
42.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1166079
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
138176
 
11.8%
e 116696
 
10.0%
n 74263
 
6.4%
i 64272
 
5.5%
r 61609
 
5.3%
t 53417
 
4.6%
a 53150
 
4.6%
u 38357
 
3.3%
l 36797
 
3.2%
m 36248
 
3.1%
Other values (166) 493094
42.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1166079
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
138176
 
11.8%
e 116696
 
10.0%
n 74263
 
6.4%
i 64272
 
5.5%
r 61609
 
5.3%
t 53417
 
4.6%
a 53150
 
4.6%
u 38357
 
3.3%
l 36797
 
3.2%
m 36248
 
3.1%
Other values (166) 493094
42.3%

description
Text

MISSING 

Distinct25585
Distinct (%)95.8%
Missing943
Missing (%)3.4%
Memory size216.2 KiB
2024-07-03T14:13:27.486944image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length9921
Median length2629
Mean length1071.8127
Min length1

Characters and Unicode

Total characters28632404
Distinct characters362
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24906 ?
Unique (%)93.2%

Sample

1st rowGrosszügiger und Lichtdurchfluteter Büro-/ Praxisraum in unmittelbarerer Nähe zum Bahnhof St. Margrethen. Aufgrund der idealen Lage, bietet der Standort auch einen zentralen Anschluss an die Autobahn sowie zahlreiche Verpflegungsmöglichkeiten. * Flexible, nutzerspezifische Raumeinteilung möglich * Hochwertiger Ausbaustandard * Deckenelemente für Lüftung und Kühlung * Eigene Toilette * Büroküche * Für technische Installationen "doppelter" Boden * Lift * Tiefe HK-/NK-Kosten (Minergie) * Parkplätze können dazu gemietet werden CHF 60.00/Mt. * Archivräume (ca. 8qm) können dazu gemietet werden CHF 56.00/Mt. * Verfügbar nach Vereinbarung * Mietzins auf Anfrage Haben wir Ihr Interesse geweckt so zögern Sie nicht uns für einen Besichtigungstermin zu kontaktieren.
2nd rowDiverse Einstellhallenparkplätze zentral in Lenzburg zu vermieten! Sie benötigen kurz- oder langfristig Parkplätze? Oder Sie benötigen für ein grösseres Projekt wie einen Umbau ausreichend Parkmöglichkeiten? Wir bieten Ihnen einzelne oder mehrere Parkplätze in der Einstellhalle an. Diese können ab CHF 130.00 pro Monat gemietet werden.
3rd rowNach Vereinbarung vermieten wir im äusserst modernen Gebäudekomplex Hochbord dieses Atelier. Das Atelier mit insgesamt 119 m2 bietet Ihnen folgende Vorzüge: * Eigene Toilette * Bodenheizung in allen Räumen * Zwei einzelne Büroräume * Glattalbahn/ Ringwiesen in unmittelbarer Nähe * Bahnhof Stettbach in Gehdistanz * Autobahnanschluss in 3 Fahrminuten Dieses Atelier eignet sich ausgezeichnet für verschiedene Nutzungen, als Büro, Verkaufsraum u.v.a.m. Prägen Sie das Hochbord mit Ihrem Businesskonzept - wir sind gespannt darauf. Haben wir Interesse geweckt? Dann freuen wir uns schon auf Ihre Kontaktanfrage über das Kontaktformular!
4th rowIn dieser sehr schönen Wohnung vermieten wir drei möbilierte WG- Zimmer. Das Haus liegt im grünen aber trotzdem nur 2min von der Bushaltestelle entfernt, der direkt zur Uni und ins Zentrum fährt.
5th rowI am looking for a buoyant, easygoing person, who is looking for such a one co-habit with me (f, 50, author and therapist),my daughter (16) and our very cuddly cat. Our beautiful and very spacious 5,5 Duplex-Apartment (177m2, parquet flooring, own washing machine and dryer, 3 bathrooms, balcony and a huge roof-terrace) is situated in the new trend quarter Westside at Escherwyssplatz – very centric and still surprisingly quite. Your room: Furnished, around 18m2 and very bright (huge window, entering morning sun.
ValueCountFrequency (%)
170789
 
4.4%
und 95531
 
2.4%
de 57923
 
1.5%
mit 55095
 
1.4%
die 53368
 
1.4%
in 50843
 
1.3%
der 42694
 
1.1%
sie 36761
 
0.9%
für 30104
 
0.8%
im 25791
 
0.7%
Other values (98993) 3303229
84.2%
2024-07-03T14:13:28.345300image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4601001
16.1%
e 3255087
 
11.4%
n 1942523
 
6.8%
i 1672186
 
5.8%
r 1466089
 
5.1%
t 1424617
 
5.0%
a 1264369
 
4.4%
s 1228618
 
4.3%
u 946952
 
3.3%
o 837961
 
2.9%
Other values (352) 9993001
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28632404
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4601001
16.1%
e 3255087
 
11.4%
n 1942523
 
6.8%
i 1672186
 
5.8%
r 1466089
 
5.1%
t 1424617
 
5.0%
a 1264369
 
4.4%
s 1228618
 
4.3%
u 946952
 
3.3%
o 837961
 
2.9%
Other values (352) 9993001
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28632404
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4601001
16.1%
e 3255087
 
11.4%
n 1942523
 
6.8%
i 1672186
 
5.8%
r 1466089
 
5.1%
t 1424617
 
5.0%
a 1264369
 
4.4%
s 1228618
 
4.3%
u 946952
 
3.3%
o 837961
 
2.9%
Other values (352) 9993001
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28632404
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4601001
16.1%
e 3255087
 
11.4%
n 1942523
 
6.8%
i 1672186
 
5.8%
r 1466089
 
5.1%
t 1424617
 
5.0%
a 1264369
 
4.4%
s 1228618
 
4.3%
u 946952
 
3.3%
o 837961
 
2.9%
Other values (352) 9993001
34.9%

surface_living
Real number (ℝ)

MISSING 

Distinct457
Distinct (%)2.8%
Missing11073
Missing (%)40.0%
Infinite0
Infinite (%)0.0%
Mean99.741136
Minimum0
Maximum3084
Zeros147
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size216.2 KiB
2024-07-03T14:13:28.605629image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18
Q161
median86
Q3120
95-th percentile210
Maximum3084
Range3084
Interquartile range (IQR)59

Descriptive statistics

Standard deviation81.816745
Coefficient of variation (CV)0.82029088
Kurtosis163.26695
Mean99.741136
Median Absolute Deviation (MAD)28
Skewness7.9065889
Sum1654107
Variance6693.9797
MonotonicityNot monotonic
2024-07-03T14:13:28.850926image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 397
 
1.4%
100 376
 
1.4%
80 369
 
1.3%
90 346
 
1.3%
75 293
 
1.1%
120 287
 
1.0%
85 270
 
1.0%
60 266
 
1.0%
65 253
 
0.9%
110 229
 
0.8%
Other values (447) 13498
48.8%
(Missing) 11073
40.0%
ValueCountFrequency (%)
0 147
0.5%
1 1
 
< 0.1%
8 6
 
< 0.1%
9 9
 
< 0.1%
10 48
 
0.2%
11 31
 
0.1%
12 84
0.3%
13 54
 
0.2%
14 103
0.4%
15 112
0.4%
ValueCountFrequency (%)
3084 1
< 0.1%
1707 1
< 0.1%
1655 1
< 0.1%
1630 1
< 0.1%
1392 1
< 0.1%
1337 1
< 0.1%
1275 1
< 0.1%
1254 1
< 0.1%
1200 1
< 0.1%
1152 1
< 0.1%

surface_property
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct1277
Distinct (%)37.3%
Missing24232
Missing (%)87.6%
Infinite0
Infinite (%)0.0%
Mean784.67328
Minimum0
Maximum90000
Zeros651
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size216.2 KiB
2024-07-03T14:13:29.106647image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q194
median426
Q3856
95-th percentile2284
Maximum90000
Range90000
Interquartile range (IQR)762

Descriptive statistics

Standard deviation2461.0398
Coefficient of variation (CV)3.1363879
Kurtosis692.55006
Mean784.67328
Median Absolute Deviation (MAD)367
Skewness22.753375
Sum2687506
Variance6056716.7
MonotonicityNot monotonic
2024-07-03T14:13:29.356092image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 651
 
2.4%
500 25
 
0.1%
1000 23
 
0.1%
100 21
 
0.1%
400 20
 
0.1%
200 19
 
0.1%
300 18
 
0.1%
150 17
 
0.1%
600 15
 
0.1%
250 14
 
0.1%
Other values (1267) 2602
 
9.4%
(Missing) 24232
87.6%
ValueCountFrequency (%)
0 651
2.4%
1 4
 
< 0.1%
2 2
 
< 0.1%
5 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 2
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
12 2
 
< 0.1%
ValueCountFrequency (%)
90000 1
< 0.1%
66596 1
< 0.1%
40000 1
< 0.1%
35048 1
< 0.1%
27000 1
< 0.1%
26456 1
< 0.1%
24521 1
< 0.1%
15182 1
< 0.1%
11475 1
< 0.1%
10267 1
< 0.1%

surface_usable
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct762
Distinct (%)9.5%
Missing19599
Missing (%)70.9%
Infinite0
Infinite (%)0.0%
Mean172.40072
Minimum0
Maximum23000
Zeros1402
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size216.2 KiB
2024-07-03T14:13:29.595239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114
median81
Q3163
95-th percentile550
Maximum23000
Range23000
Interquartile range (IQR)149

Descriptive statistics

Standard deviation557.40949
Coefficient of variation (CV)3.2332202
Kurtosis671.82185
Mean172.40072
Median Absolute Deviation (MAD)69
Skewness21.033307
Sum1389205
Variance310705.34
MonotonicityNot monotonic
2024-07-03T14:13:29.847817image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1402
 
5.1%
12 204
 
0.7%
100 145
 
0.5%
120 96
 
0.3%
13 93
 
0.3%
80 90
 
0.3%
14 90
 
0.3%
70 88
 
0.3%
90 87
 
0.3%
50 74
 
0.3%
Other values (752) 5689
 
20.6%
(Missing) 19599
70.9%
ValueCountFrequency (%)
0 1402
5.1%
1 17
 
0.1%
2 29
 
0.1%
3 14
 
0.1%
4 10
 
< 0.1%
5 10
 
< 0.1%
6 20
 
0.1%
7 22
 
0.1%
8 25
 
0.1%
9 18
 
0.1%
ValueCountFrequency (%)
23000 1
< 0.1%
18600 1
< 0.1%
18000 1
< 0.1%
11000 1
< 0.1%
8033 1
< 0.1%
8000 2
< 0.1%
6616 1
< 0.1%
6500 1
< 0.1%
5443 2
< 0.1%
5421 1
< 0.1%

surface_usable_minimum
Real number (ℝ)

MISSING 

Distinct129
Distinct (%)38.2%
Missing27319
Missing (%)98.8%
Infinite0
Infinite (%)0.0%
Mean114.27811
Minimum5
Maximum1582
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.2 KiB
2024-07-03T14:13:30.081518image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile12
Q124.25
median65
Q3124.75
95-th percentile306
Maximum1582
Range1577
Interquartile range (IQR)100.5

Descriptive statistics

Standard deviation180.75839
Coefficient of variation (CV)1.5817412
Kurtosis27.148119
Mean114.27811
Median Absolute Deviation (MAD)45
Skewness4.7072285
Sum38626
Variance32673.596
MonotonicityNot monotonic
2024-07-03T14:13:30.347498image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 18
 
0.1%
12 13
 
< 0.1%
80 11
 
< 0.1%
100 10
 
< 0.1%
50 10
 
< 0.1%
16 9
 
< 0.1%
18 9
 
< 0.1%
60 9
 
< 0.1%
90 8
 
< 0.1%
75 8
 
< 0.1%
Other values (119) 233
 
0.8%
(Missing) 27319
98.8%
ValueCountFrequency (%)
5 1
 
< 0.1%
8 4
 
< 0.1%
9 1
 
< 0.1%
10 3
 
< 0.1%
11 4
 
< 0.1%
12 13
< 0.1%
13 1
 
< 0.1%
14 1
 
< 0.1%
15 7
< 0.1%
16 9
< 0.1%
ValueCountFrequency (%)
1582 1
< 0.1%
1265 1
< 0.1%
1200 1
< 0.1%
1100 1
< 0.1%
1099 1
< 0.1%
980 1
< 0.1%
785 1
< 0.1%
600 1
< 0.1%
585 1
< 0.1%
519 1
< 0.1%

volume
Real number (ℝ)

MISSING  ZEROS 

Distinct917
Distinct (%)25.5%
Missing24061
Missing (%)87.0%
Infinite0
Infinite (%)0.0%
Mean508.7475
Minimum0
Maximum56232
Zeros2127
Zeros (%)7.7%
Negative0
Negative (%)0.0%
Memory size216.2 KiB
2024-07-03T14:13:30.603899image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3757.25
95-th percentile1841.5
Maximum56232
Range56232
Interquartile range (IQR)757.25

Descriptive statistics

Standard deviation1518.8184
Coefficient of variation (CV)2.9854072
Kurtosis541.55045
Mean508.7475
Median Absolute Deviation (MAD)0
Skewness17.707784
Sum1829456
Variance2306809.4
MonotonicityNot monotonic
2024-07-03T14:13:30.849068image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2127
 
7.7%
800 14
 
0.1%
850 13
 
< 0.1%
900 11
 
< 0.1%
500 10
 
< 0.1%
700 9
 
< 0.1%
180 9
 
< 0.1%
780 9
 
< 0.1%
840 8
 
< 0.1%
860 7
 
< 0.1%
Other values (907) 1379
 
5.0%
(Missing) 24061
87.0%
ValueCountFrequency (%)
0 2127
7.7%
3 2
 
< 0.1%
8 2
 
< 0.1%
10 1
 
< 0.1%
16 1
 
< 0.1%
18 1
 
< 0.1%
20 1
 
< 0.1%
24 1
 
< 0.1%
25 2
 
< 0.1%
28 1
 
< 0.1%
ValueCountFrequency (%)
56232 1
< 0.1%
21567 1
< 0.1%
19742 1
< 0.1%
18000 1
< 0.1%
17777 1
< 0.1%
15910 1
< 0.1%
15528 1
< 0.1%
13135 1
< 0.1%
11587 1
< 0.1%
11409 2
< 0.1%

space_display
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct928
Distinct (%)4.2%
Missing5616
Missing (%)20.3%
Infinite0
Infinite (%)0.0%
Mean142.57652
Minimum0
Maximum90000
Zeros1235
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size216.2 KiB
2024-07-03T14:13:31.095511image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q154
median86
Q3128
95-th percentile335
Maximum90000
Range90000
Interquartile range (IQR)74

Descriptive statistics

Standard deviation724.20738
Coefficient of variation (CV)5.0794296
Kurtosis10846.974
Mean142.57652
Median Absolute Deviation (MAD)36
Skewness90.801785
Sum3142529
Variance524476.32
MonotonicityNot monotonic
2024-07-03T14:13:31.342428image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1235
 
4.5%
100 441
 
1.6%
70 430
 
1.6%
80 401
 
1.4%
90 372
 
1.3%
120 328
 
1.2%
75 320
 
1.2%
60 301
 
1.1%
85 282
 
1.0%
65 280
 
1.0%
Other values (918) 17651
63.8%
(Missing) 5616
 
20.3%
ValueCountFrequency (%)
0 1235
4.5%
1 17
 
0.1%
2 28
 
0.1%
3 13
 
< 0.1%
4 10
 
< 0.1%
5 6
 
< 0.1%
6 14
 
0.1%
7 17
 
0.1%
8 29
 
0.1%
9 23
 
0.1%
ValueCountFrequency (%)
90000 1
< 0.1%
23000 1
< 0.1%
18600 1
< 0.1%
18000 1
< 0.1%
11475 1
< 0.1%
11000 1
< 0.1%
10267 1
< 0.1%
8033 1
< 0.1%
8000 2
< 0.1%
6629 1
< 0.1%

number_of_rooms
Real number (ℝ)

MISSING 

Distinct50
Distinct (%)0.3%
Missing9351
Missing (%)33.8%
Infinite0
Infinite (%)0.0%
Mean3.5861466
Minimum1
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.2 KiB
2024-07-03T14:13:32.283761image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12.5
median3.5
Q34.5
95-th percentile6.5
Maximum75
Range74
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1418317
Coefficient of variation (CV)0.59725158
Kurtosis126.80191
Mean3.5861466
Median Absolute Deviation (MAD)1
Skewness5.9453982
Sum65648
Variance4.5874431
MonotonicityNot monotonic
2024-07-03T14:13:32.546172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.5 3929
14.2%
4.5 3321
 
12.0%
1 2474
 
8.9%
2.5 2401
 
8.7%
5.5 1348
 
4.9%
3 1116
 
4.0%
2 770
 
2.8%
4 720
 
2.6%
1.5 629
 
2.3%
6.5 459
 
1.7%
Other values (40) 1139
 
4.1%
(Missing) 9351
33.8%
ValueCountFrequency (%)
1 2474
8.9%
1.5 629
 
2.3%
2 770
 
2.8%
2.5 2401
8.7%
3 1116
 
4.0%
3.5 3929
14.2%
4 720
 
2.6%
4.5 3321
12.0%
5 228
 
0.8%
5.5 1348
 
4.9%
ValueCountFrequency (%)
75 1
 
< 0.1%
64 1
 
< 0.1%
45 1
 
< 0.1%
33 1
 
< 0.1%
32 1
 
< 0.1%
30 1
 
< 0.1%
28 2
 
< 0.1%
27.5 1
 
< 0.1%
25 3
< 0.1%
24 6
< 0.1%

floor
Real number (ℝ)

MISSING  ZEROS 

Distinct33
Distinct (%)0.2%
Missing7885
Missing (%)28.5%
Infinite0
Infinite (%)0.0%
Mean1.6139996
Minimum-8
Maximum31
Zeros3501
Zeros (%)12.7%
Negative2390
Negative (%)8.6%
Memory size216.2 KiB
2024-07-03T14:13:32.790860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-8
5-th percentile-1
Q10
median1
Q33
95-th percentile5
Maximum31
Range39
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1987168
Coefficient of variation (CV)1.3622784
Kurtosis13.451046
Mean1.6139996
Median Absolute Deviation (MAD)1
Skewness2.1838679
Sum31912
Variance4.8343556
MonotonicityNot monotonic
2024-07-03T14:13:33.019296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1 4562
16.5%
2 4048
14.6%
0 3501
12.7%
3 2631
 
9.5%
-1 1932
 
7.0%
4 1296
 
4.7%
5 636
 
2.3%
-2 320
 
1.2%
6 277
 
1.0%
7 121
 
0.4%
Other values (23) 448
 
1.6%
(Missing) 7885
28.5%
ValueCountFrequency (%)
-8 2
 
< 0.1%
-6 2
 
< 0.1%
-5 7
 
< 0.1%
-4 46
 
0.2%
-3 81
 
0.3%
-2 320
 
1.2%
-1 1932
7.0%
0 3501
12.7%
1 4562
16.5%
2 4048
14.6%
ValueCountFrequency (%)
31 1
 
< 0.1%
25 1
 
< 0.1%
24 3
 
< 0.1%
23 3
 
< 0.1%
22 2
 
< 0.1%
21 2
 
< 0.1%
19 6
< 0.1%
18 8
< 0.1%
17 4
 
< 0.1%
16 11
< 0.1%
Distinct7593
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Memory size216.2 KiB
2024-07-03T14:13:33.284150image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length508
Median length368
Mean length69.271215
Min length2

Characters and Unicode

Total characters1915834
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6029 ?
Unique (%)21.8%

Sample

1st row[{'name': 'petsallowed'}, {'name': 'view'}]
2nd row[]
3rd row[{'name': 'lift'}, {'name': 'garage'}]
4th row[{'name': 'parkingspace'}, {'name': 'view'}, {'name': 'dishwasher'}]
5th row[{'name': 'tumbler'}, {'name': 'parquetflooring'}, {'name': 'washingmachine'}, {'name': 'broadbandinternet'}, {'name': 'cable'}, {'name': 'dishwasher'}, {'name': 'lift'}, {'name': 'balconygarden'}]
ValueCountFrequency (%)
name 79570
47.8%
balconygarden 11621
 
7.0%
lift 9063
 
5.4%
parkingspace 8847
 
5.3%
7194
 
4.3%
garage 7068
 
4.2%
view 6062
 
3.6%
cable 5524
 
3.3%
petsallowed 5357
 
3.2%
dishwasher 4633
 
2.8%
Other values (13) 21395
 
12.9%
2024-07-03T14:13:33.861846image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 318280
16.6%
a 175292
 
9.1%
e 169416
 
8.8%
138677
 
7.2%
n 134132
 
7.0%
m 88555
 
4.6%
{ 79570
 
4.2%
: 79570
 
4.2%
} 79570
 
4.2%
, 59107
 
3.1%
Other values (21) 593665
31.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1915834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 318280
16.6%
a 175292
 
9.1%
e 169416
 
8.8%
138677
 
7.2%
n 134132
 
7.0%
m 88555
 
4.6%
{ 79570
 
4.2%
: 79570
 
4.2%
} 79570
 
4.2%
, 59107
 
3.1%
Other values (21) 593665
31.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1915834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 318280
16.6%
a 175292
 
9.1%
e 169416
 
8.8%
138677
 
7.2%
n 134132
 
7.0%
m 88555
 
4.6%
{ 79570
 
4.2%
: 79570
 
4.2%
} 79570
 
4.2%
, 59107
 
3.1%
Other values (21) 593665
31.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1915834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 318280
16.6%
a 175292
 
9.1%
e 169416
 
8.8%
138677
 
7.2%
n 134132
 
7.0%
m 88555
 
4.6%
{ 79570
 
4.2%
: 79570
 
4.2%
} 79570
 
4.2%
, 59107
 
3.1%
Other values (21) 593665
31.0%

is_furnished
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.1 KiB
False
24631 
True
3026 
ValueCountFrequency (%)
False 24631
89.1%
True 3026
 
10.9%
2024-07-03T14:13:34.067898image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

is_temporary
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.1 KiB
False
26293 
True
 
1364
ValueCountFrequency (%)
False 26293
95.1%
True 1364
 
4.9%
2024-07-03T14:13:34.226903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

is_selling_furniture
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.1 KiB
False
27140 
True
 
517
ValueCountFrequency (%)
False 27140
98.1%
True 517
 
1.9%
2024-07-03T14:13:34.372433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

street
Text

MISSING 

Distinct15658
Distinct (%)68.0%
Missing4637
Missing (%)16.8%
Memory size216.2 KiB
2024-07-03T14:13:34.802141image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length71
Median length50
Mean length16.694136
Min length1

Characters and Unicode

Total characters384299
Distinct characters103
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12673 ?
Unique (%)55.1%

Sample

1st rowParkstr. 12
2nd rowMartha Ringier-Strasse
3rd rowSonnentalstrasse 10
4th rowWaldstrasse 5
5th rowLoft, Valangines
ValueCountFrequency (%)
de 1474
 
2.6%
auf 1292
 
2.3%
anfrage 1287
 
2.3%
rue 1254
 
2.2%
1 870
 
1.6%
2 768
 
1.4%
du 706
 
1.3%
4 641
 
1.1%
5 613
 
1.1%
des 602
 
1.1%
Other values (9858) 46372
83.0%
2024-07-03T14:13:35.583732image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 43160
 
11.2%
s 40414
 
10.5%
32910
 
8.6%
r 28360
 
7.4%
a 27777
 
7.2%
t 21645
 
5.6%
n 16075
 
4.2%
l 11121
 
2.9%
i 11038
 
2.9%
u 10622
 
2.8%
Other values (93) 141177
36.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 384299
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 43160
 
11.2%
s 40414
 
10.5%
32910
 
8.6%
r 28360
 
7.4%
a 27777
 
7.2%
t 21645
 
5.6%
n 16075
 
4.2%
l 11121
 
2.9%
i 11038
 
2.9%
u 10622
 
2.8%
Other values (93) 141177
36.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 384299
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 43160
 
11.2%
s 40414
 
10.5%
32910
 
8.6%
r 28360
 
7.4%
a 27777
 
7.2%
t 21645
 
5.6%
n 16075
 
4.2%
l 11121
 
2.9%
i 11038
 
2.9%
u 10622
 
2.8%
Other values (93) 141177
36.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 384299
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 43160
 
11.2%
s 40414
 
10.5%
32910
 
8.6%
r 28360
 
7.4%
a 27777
 
7.2%
t 21645
 
5.6%
n 16075
 
4.2%
l 11121
 
2.9%
i 11038
 
2.9%
u 10622
 
2.8%
Other values (93) 141177
36.7%

zipcode
Real number (ℝ)

Distinct2281
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5351.5373
Minimum1000
Maximum88100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.2 KiB
2024-07-03T14:13:35.849210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1196
Q13011
median5012
Q38107
95-th percentile9050
Maximum88100
Range87100
Interquartile range (IQR)5096

Descriptive statistics

Standard deviation2807.9152
Coefficient of variation (CV)0.52469319
Kurtosis25.746764
Mean5351.5373
Median Absolute Deviation (MAD)2996
Skewness0.88098909
Sum1.4800747 × 108
Variance7884387.6
MonotonicityNot monotonic
2024-07-03T14:13:36.098883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9000 460
 
1.7%
1700 383
 
1.4%
8050 286
 
1.0%
2300 276
 
1.0%
8004 248
 
0.9%
8400 199
 
0.7%
4052 193
 
0.7%
8048 186
 
0.7%
4123 169
 
0.6%
8008 164
 
0.6%
Other values (2271) 25093
90.7%
ValueCountFrequency (%)
1000 9
 
< 0.1%
1001 1
 
< 0.1%
1002 2
 
< 0.1%
1003 100
0.4%
1004 93
0.3%
1005 45
0.2%
1006 47
0.2%
1007 71
0.3%
1008 69
0.2%
1009 51
0.2%
ValueCountFrequency (%)
88100 1
 
< 0.1%
9658 1
 
< 0.1%
9657 3
 
< 0.1%
9656 5
 
< 0.1%
9650 5
 
< 0.1%
9643 4
 
< 0.1%
9642 2
 
< 0.1%
9630 23
0.1%
9621 1
 
< 0.1%
9620 14
0.1%

city
Text

Distinct2928
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size216.2 KiB
2024-07-03T14:13:36.612450image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length58
Median length30
Mean length8.295079
Min length2

Characters and Unicode

Total characters229417
Distinct characters97
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1090 ?
Unique (%)3.9%

Sample

1st rowSt. Margrethen SG
2nd rowLenzburg
3rd rowDübendorf
4th rowSt. Gallen
5th rowZürich
ValueCountFrequency (%)
zürich 2138
 
6.7%
basel 1058
 
3.3%
st 751
 
2.4%
gallen 697
 
2.2%
bern 661
 
2.1%
lausanne 522
 
1.6%
zurich 488
 
1.5%
winterthur 412
 
1.3%
fribourg 384
 
1.2%
la 353
 
1.1%
Other values (2611) 24444
76.6%
2024-07-03T14:13:37.395530image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 25043
 
10.9%
n 21053
 
9.2%
r 16082
 
7.0%
i 14856
 
6.5%
a 13608
 
5.9%
l 13275
 
5.8%
s 9799
 
4.3%
h 9360
 
4.1%
t 9200
 
4.0%
o 8218
 
3.6%
Other values (87) 88923
38.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 229417
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 25043
 
10.9%
n 21053
 
9.2%
r 16082
 
7.0%
i 14856
 
6.5%
a 13608
 
5.9%
l 13275
 
5.8%
s 9799
 
4.3%
h 9360
 
4.1%
t 9200
 
4.0%
o 8218
 
3.6%
Other values (87) 88923
38.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 229417
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 25043
 
10.9%
n 21053
 
9.2%
r 16082
 
7.0%
i 14856
 
6.5%
a 13608
 
5.9%
l 13275
 
5.8%
s 9799
 
4.3%
h 9360
 
4.1%
t 9200
 
4.0%
o 8218
 
3.6%
Other values (87) 88923
38.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 229417
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 25043
 
10.9%
n 21053
 
9.2%
r 16082
 
7.0%
i 14856
 
6.5%
a 13608
 
5.9%
l 13275
 
5.8%
s 9799
 
4.3%
h 9360
 
4.1%
t 9200
 
4.0%
o 8218
 
3.6%
Other values (87) 88923
38.8%
Distinct18900
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Memory size216.2 KiB
2024-07-03T14:13:37.991755image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length82
Median length60
Mean length28.854973
Min length7

Characters and Unicode

Total characters798042
Distinct characters118
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14963 ?
Unique (%)54.1%

Sample

1st rowParkstr. 12, 9430 St. Margrethen SG
2nd rowMartha Ringier-Strasse, 5600 Lenzburg
3rd rowSonnentalstrasse 10, 8600 Dübendorf
4th rowWaldstrasse 5, 9008 St. Gallen
5th row8005 Zürich
ValueCountFrequency (%)
zürich 2145
 
1.9%
de 1478
 
1.3%
auf 1292
 
1.1%
anfrage 1287
 
1.1%
rue 1255
 
1.1%
basel 1068
 
0.9%
la 938
 
0.8%
st 885
 
0.8%
1 871
 
0.8%
2 770
 
0.7%
Other values (14018) 103455
89.6%
2024-07-03T14:13:38.904217image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
87840
 
11.0%
e 68203
 
8.5%
s 50213
 
6.3%
r 44442
 
5.6%
a 41385
 
5.2%
n 37128
 
4.7%
t 30845
 
3.9%
0 28138
 
3.5%
i 25894
 
3.2%
l 24396
 
3.1%
Other values (108) 359558
45.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 798042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
87840
 
11.0%
e 68203
 
8.5%
s 50213
 
6.3%
r 44442
 
5.6%
a 41385
 
5.2%
n 37128
 
4.7%
t 30845
 
3.9%
0 28138
 
3.5%
i 25894
 
3.2%
l 24396
 
3.1%
Other values (108) 359558
45.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 798042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
87840
 
11.0%
e 68203
 
8.5%
s 50213
 
6.3%
r 44442
 
5.6%
a 41385
 
5.2%
n 37128
 
4.7%
t 30845
 
3.9%
0 28138
 
3.5%
i 25894
 
3.2%
l 24396
 
3.1%
Other values (108) 359558
45.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 798042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
87840
 
11.0%
e 68203
 
8.5%
s 50213
 
6.3%
r 44442
 
5.6%
a 41385
 
5.2%
n 37128
 
4.7%
t 30845
 
3.9%
0 28138
 
3.5%
i 25894
 
3.2%
l 24396
 
3.1%
Other values (108) 359558
45.1%

latitude
Real number (ℝ)

Distinct20905
Distinct (%)75.6%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean47.092183
Minimum45.826182
Maximum47.793652
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.2 KiB
2024-07-03T14:13:39.168540image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum45.826182
5-th percentile46.193779
Q146.835732
median47.271607
Q347.423097
95-th percentile47.559132
Maximum47.793652
Range1.96747
Interquartile range (IQR)0.587365

Descriptive statistics

Standard deviation0.44948791
Coefficient of variation (CV)0.0095448518
Kurtosis-0.25956045
Mean47.092183
Median Absolute Deviation (MAD)0.21971
Skewness-0.95573521
Sum1302381.4
Variance0.20203938
MonotonicityNot monotonic
2024-07-03T14:13:39.436393image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46.77822161 28
 
0.1%
47.11767161 26
 
0.1%
47.36689161 24
 
0.1%
46.0127616 23
 
0.1%
46.20913161 23
 
0.1%
47.43064161 22
 
0.1%
46.78719161 22
 
0.1%
46.2916616 21
 
0.1%
47.4241816 21
 
0.1%
47.1131916 20
 
0.1%
Other values (20895) 27426
99.2%
ValueCountFrequency (%)
45.8261816 1
 
< 0.1%
45.82732161 1
 
< 0.1%
45.8310216 1
 
< 0.1%
45.8322416 4
< 0.1%
45.8329516 2
< 0.1%
45.8332216 1
 
< 0.1%
45.83389161 2
< 0.1%
45.8353816 1
 
< 0.1%
45.8357516 3
< 0.1%
45.83580161 1
 
< 0.1%
ValueCountFrequency (%)
47.79365161 1
 
< 0.1%
47.7680716 1
 
< 0.1%
47.7680316 1
 
< 0.1%
47.75681161 1
 
< 0.1%
47.7566216 1
 
< 0.1%
47.7564616 7
< 0.1%
47.7534916 1
 
< 0.1%
47.75052161 1
 
< 0.1%
47.75009161 3
< 0.1%
47.75003161 1
 
< 0.1%

longitude
Real number (ℝ)

Distinct21403
Distinct (%)77.4%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean7.9857019
Minimum5.9796912
Maximum10.403751
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.2 KiB
2024-07-03T14:13:39.716865image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum5.9796912
5-th percentile6.5662112
Q17.4049287
median8.0184862
Q38.5922062
95-th percentile9.3832712
Maximum10.403751
Range4.42406
Interquartile range (IQR)1.1872775

Descriptive statistics

Standard deviation0.86136894
Coefficient of variation (CV)0.1078639
Kurtosis-0.75224161
Mean7.9857019
Median Absolute Deviation (MAD)0.589425
Skewness-0.16865379
Sum220852.57
Variance0.74195645
MonotonicityNot monotonic
2024-07-03T14:13:39.970544image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.840561235 40
 
0.1%
7.156761234 28
 
0.1%
7.332471234 24
 
0.1%
7.316841238 23
 
0.1%
8.96301124 23
 
0.1%
6.807401235 22
 
0.1%
9.383271235 22
 
0.1%
7.542931234 22
 
0.1%
8.558061235 21
 
0.1%
7.301061235 20
 
0.1%
Other values (21393) 27411
99.1%
ValueCountFrequency (%)
5.979691235 1
< 0.1%
5.991881235 1
< 0.1%
5.993681235 1
< 0.1%
6.002451235 2
< 0.1%
6.007011236 1
< 0.1%
6.019591235 1
< 0.1%
6.019631235 1
< 0.1%
6.036691235 1
< 0.1%
6.037741235 1
< 0.1%
6.037791236 2
< 0.1%
ValueCountFrequency (%)
10.40375123 1
< 0.1%
10.36431123 1
< 0.1%
10.35798123 1
< 0.1%
10.30210124 2
< 0.1%
10.29604123 1
< 0.1%
10.14259123 1
< 0.1%
10.09659123 1
< 0.1%
9.958821237 1
< 0.1%
9.902851236 1
< 0.1%
9.899801236 1
< 0.1%

year_built
Real number (ℝ)

MISSING  SKEWED 

Distinct281
Distinct (%)2.2%
Missing14588
Missing (%)52.7%
Infinite0
Infinite (%)0.0%
Mean3480.6475
Minimum0
Maximum19702024
Zeros276
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size216.2 KiB
2024-07-03T14:13:40.215880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1889.4
Q11968
median1991
Q32017
95-th percentile2024
Maximum19702024
Range19702024
Interquartile range (IQR)49

Descriptive statistics

Standard deviation172342.3
Coefficient of variation (CV)49.514437
Kurtosis13063.497
Mean3480.6475
Median Absolute Deviation (MAD)25
Skewness114.28386
Sum45488582
Variance2.9701869 × 1010
MonotonicityNot monotonic
2024-07-03T14:13:40.496405image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2024 994
 
3.6%
2023 586
 
2.1%
2022 315
 
1.1%
0 276
 
1.0%
2018 243
 
0.9%
1970 234
 
0.8%
2017 226
 
0.8%
2019 220
 
0.8%
2015 214
 
0.8%
2021 204
 
0.7%
Other values (271) 9557
34.6%
(Missing) 14588
52.7%
ValueCountFrequency (%)
0 276
1.0%
6 2
 
< 0.1%
177 1
 
< 0.1%
199 1
 
< 0.1%
1250 1
 
< 0.1%
1255 1
 
< 0.1%
1300 1
 
< 0.1%
1350 1
 
< 0.1%
1369 1
 
< 0.1%
1400 5
 
< 0.1%
ValueCountFrequency (%)
19702024 1
 
< 0.1%
202300 2
 
< 0.1%
20220 1
 
< 0.1%
2997 1
 
< 0.1%
2027 7
 
< 0.1%
2026 105
 
0.4%
2025 187
 
0.7%
2024 994
3.6%
2023 586
2.1%
2022 315
 
1.1%

year_renovated
Real number (ℝ)

MISSING  ZEROS 

Distinct69
Distinct (%)1.3%
Missing22187
Missing (%)80.2%
Infinite0
Infinite (%)0.0%
Mean1909.9135
Minimum0
Maximum2027
Zeros285
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size216.2 KiB
2024-07-03T14:13:40.769936image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12009.25
median2018
Q32022
95-th percentile2024
Maximum2027
Range2027
Interquartile range (IQR)12.75

Descriptive statistics

Standard deviation448.73443
Coefficient of variation (CV)0.23495013
Kurtosis14.178758
Mean1909.9135
Median Absolute Deviation (MAD)5
Skewness-4.0204573
Sum10447227
Variance201362.58
MonotonicityNot monotonic
2024-07-03T14:13:41.043328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2024 721
 
2.6%
2023 592
 
2.1%
2022 450
 
1.6%
2020 308
 
1.1%
2021 294
 
1.1%
0 285
 
1.0%
2019 263
 
1.0%
2018 249
 
0.9%
2017 225
 
0.8%
2015 179
 
0.6%
Other values (59) 1904
 
6.9%
(Missing) 22187
80.2%
ValueCountFrequency (%)
0 285
1.0%
24 1
 
< 0.1%
1907 1
 
< 0.1%
1916 1
 
< 0.1%
1930 1
 
< 0.1%
1942 1
 
< 0.1%
1950 1
 
< 0.1%
1958 4
 
< 0.1%
1959 1
 
< 0.1%
1960 1
 
< 0.1%
ValueCountFrequency (%)
2027 1
 
< 0.1%
2025 25
 
0.1%
2024 721
2.6%
2023 592
2.1%
2022 450
1.6%
2021 294
1.1%
2020 308
1.1%
2019 263
 
1.0%
2018 249
 
0.9%
2017 225
 
0.8%

moving_date_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size216.2 KiB
agr
10928 
imm
8607 
dat
8122 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters82971
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowagr
2nd rowagr
3rd rowimm
4th rowdat
5th rowdat

Common Values

ValueCountFrequency (%)
agr 10928
39.5%
imm 8607
31.1%
dat 8122
29.4%

Length

2024-07-03T14:13:41.281683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-03T14:13:41.453559image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
agr 10928
39.5%
imm 8607
31.1%
dat 8122
29.4%

Most occurring characters

ValueCountFrequency (%)
a 19050
23.0%
m 17214
20.7%
g 10928
13.2%
r 10928
13.2%
i 8607
10.4%
d 8122
9.8%
t 8122
9.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 82971
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 19050
23.0%
m 17214
20.7%
g 10928
13.2%
r 10928
13.2%
i 8607
10.4%
d 8122
9.8%
t 8122
9.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 82971
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 19050
23.0%
m 17214
20.7%
g 10928
13.2%
r 10928
13.2%
i 8607
10.4%
d 8122
9.8%
t 8122
9.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 82971
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 19050
23.0%
m 17214
20.7%
g 10928
13.2%
r 10928
13.2%
i 8607
10.4%
d 8122
9.8%
t 8122
9.8%

moving_date
Text

MISSING 

Distinct289
Distinct (%)3.6%
Missing19535
Missing (%)70.6%
Memory size216.2 KiB
2024-07-03T14:13:41.895655image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters81220
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)1.2%

Sample

1st row2018-09-01
2nd row2023-04-01
3rd row2019-07-01
4th row2020-03-01
5th row2021-04-01
ValueCountFrequency (%)
2024-08-01 1633
20.1%
2024-09-01 1396
17.2%
2024-07-01 1129
13.9%
2024-10-01 1008
12.4%
2024-11-01 197
 
2.4%
2024-07-16 162
 
2.0%
2024-06-01 151
 
1.9%
2024-08-16 112
 
1.4%
2024-07-15 108
 
1.3%
2024-09-16 97
 
1.2%
Other values (279) 2129
26.2%
2024-07-03T14:13:42.584822image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 22629
27.9%
2 16918
20.8%
- 16244
20.0%
1 9342
11.5%
4 7655
 
9.4%
8 2259
 
2.8%
7 1959
 
2.4%
9 1778
 
2.2%
6 1005
 
1.2%
5 865
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 81220
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 22629
27.9%
2 16918
20.8%
- 16244
20.0%
1 9342
11.5%
4 7655
 
9.4%
8 2259
 
2.8%
7 1959
 
2.4%
9 1778
 
2.2%
6 1005
 
1.2%
5 865
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 81220
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 22629
27.9%
2 16918
20.8%
- 16244
20.0%
1 9342
11.5%
4 7655
 
9.4%
8 2259
 
2.8%
7 1959
 
2.4%
9 1778
 
2.2%
6 1005
 
1.2%
5 865
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 81220
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 22629
27.9%
2 16918
20.8%
- 16244
20.0%
1 9342
11.5%
4 7655
 
9.4%
8 2259
 
2.8%
7 1959
 
2.4%
9 1778
 
2.2%
6 1005
 
1.2%
5 865
 
1.1%

video_url
Text

MISSING 

Distinct156
Distinct (%)35.6%
Missing27219
Missing (%)98.4%
Memory size216.2 KiB
2024-07-03T14:13:42.942159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length61
Median length28
Mean length35.098174
Min length27

Characters and Unicode

Total characters15373
Distinct characters70
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique126 ?
Unique (%)28.8%

Sample

1st rowhttps://www.youtube.com/watch?v=sQN66oOVbgQ
2nd rowhttps://youtu.be/-ueYl9wNN-E
3rd rowhttps://www.youtube.com/watch?v=VYK-6YsqVqE&feature=emb_logo
4th rowhttps://www.youtube.com/watch?v=h0zyts9KJs0&t=1s
5th rowhttps://my.matterport.com/show/?m=G5JvFJaqqoY
ValueCountFrequency (%)
https://youtu.be/6qgcqbkx_u0 95
21.7%
https://www.youtube.com/watch?v=6qgcqbkx_u0 59
 
13.5%
https://youtu.be/7nm1nqgtdu4 40
 
9.1%
https://www.youtube.com/watch?v=pesusc6oxc8 28
 
6.4%
https://www.youtube.com/watch?v=4xhpj8olg4s 15
 
3.4%
https://youtu.be/t5lznqwkkgu 14
 
3.2%
https://youtu.be/nxrivvdqj1k 4
 
0.9%
https://youtu.be/vjtlsztko14 4
 
0.9%
https://www.youtube.com/watch?v=hfr6vsfobe4 4
 
0.9%
https://youtu.be/iujrkz7euaq 3
 
0.7%
Other values (146) 172
39.3%
2024-07-03T14:13:43.517139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 1612
 
10.5%
/ 1318
 
8.6%
u 937
 
6.1%
w 837
 
5.4%
o 713
 
4.6%
h 672
 
4.4%
. 637
 
4.1%
b 620
 
4.0%
s 518
 
3.4%
e 517
 
3.4%
Other values (60) 6992
45.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15373
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 1612
 
10.5%
/ 1318
 
8.6%
u 937
 
6.1%
w 837
 
5.4%
o 713
 
4.6%
h 672
 
4.4%
. 637
 
4.1%
b 620
 
4.0%
s 518
 
3.4%
e 517
 
3.4%
Other values (60) 6992
45.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15373
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 1612
 
10.5%
/ 1318
 
8.6%
u 937
 
6.1%
w 837
 
5.4%
o 713
 
4.6%
h 672
 
4.4%
. 637
 
4.1%
b 620
 
4.0%
s 518
 
3.4%
e 517
 
3.4%
Other values (60) 6992
45.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15373
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 1612
 
10.5%
/ 1318
 
8.6%
u 937
 
6.1%
w 837
 
5.4%
o 713
 
4.6%
h 672
 
4.4%
. 637
 
4.1%
b 620
 
4.0%
s 518
 
3.4%
e 517
 
3.4%
Other values (60) 6992
45.5%

tour_url
Text

MISSING 

Distinct1363
Distinct (%)85.9%
Missing26071
Missing (%)94.3%
Memory size216.2 KiB
2024-07-03T14:13:43.911331image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length164
Median length145
Mean length50.459647
Min length22

Characters and Unicode

Total characters80029
Distinct characters79
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1233 ?
Unique (%)77.7%

Sample

1st rowhttps://my.matterport.com/show/?m=fiUSPMkMUTc
2nd rowhttps://my.matterport.com/show/?m=h3Zmzk95A2s
3rd rowhttps://my.matterport.com/show/?m=2h7iEUpcHyL
4th rowhttps://my.matterport.com/show/?m=Srmo9JURw4C
5th rowhttps://my.matterport.com/show/?m=AsRXBrsZJxo
ValueCountFrequency (%)
https://360.casatour.ch/view/fullscreen/id/vvg80 9
 
0.6%
https://tour.beyonity.ch/?id=l_oree_aussenansicht_1/player 8
 
0.5%
https://navigator.beyonity.ch/?id=8ecc4456 7
 
0.4%
https://360.feelestate.ch/view/fullscreen/id/vvw5s 7
 
0.4%
https://projekt-interim.ch/fr/espaces-libres 6
 
0.4%
https://vtour.businessimages.ch/vtour_kirchgasse_lotzwil/tour.html 6
 
0.4%
https://my.matterport.com/show/?m=giyy2ggdt5c&ts=1 5
 
0.3%
https://360.feelestate.ch/view/fullscreen/id/vzvzm 5
 
0.3%
https://bluemenau.designraum.ch 4
 
0.3%
https://my.matterport.com/show/?m=m3bscbd2dsb 4
 
0.3%
Other values (1353) 1525
96.2%
2024-07-03T14:13:44.608104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 7623
 
9.5%
/ 6945
 
8.7%
m 4685
 
5.9%
o 4323
 
5.4%
s 3883
 
4.9%
e 3798
 
4.7%
h 3587
 
4.5%
r 3300
 
4.1%
. 2966
 
3.7%
p 2889
 
3.6%
Other values (69) 36030
45.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 80029
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 7623
 
9.5%
/ 6945
 
8.7%
m 4685
 
5.9%
o 4323
 
5.4%
s 3883
 
4.9%
e 3798
 
4.7%
h 3587
 
4.5%
r 3300
 
4.1%
. 2966
 
3.7%
p 2889
 
3.6%
Other values (69) 36030
45.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 80029
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 7623
 
9.5%
/ 6945
 
8.7%
m 4685
 
5.9%
o 4323
 
5.4%
s 3883
 
4.9%
e 3798
 
4.7%
h 3587
 
4.5%
r 3300
 
4.1%
. 2966
 
3.7%
p 2889
 
3.6%
Other values (69) 36030
45.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 80029
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 7623
 
9.5%
/ 6945
 
8.7%
m 4685
 
5.9%
o 4323
 
5.4%
s 3883
 
4.9%
e 3798
 
4.7%
h 3587
 
4.5%
r 3300
 
4.1%
. 2966
 
3.7%
p 2889
 
3.6%
Other values (69) 36030
45.0%

website_url
Text

MISSING 

Distinct5458
Distinct (%)71.7%
Missing20044
Missing (%)72.5%
Memory size216.2 KiB
2024-07-03T14:13:45.003024image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length184
Median length142
Mean length65.79246
Min length15

Characters and Unicode

Total characters500878
Distinct characters82
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4983 ?
Unique (%)65.5%

Sample

1st rowhttp://ceres-tower.ch/
2nd rowhttps://visionapartments.com/en-gb/
3rd rowhttps://visionapartments.com/en-gb/
4th rowhttps://visionapartments.com/en-gb/
5th rowhttps://visionapartments.com/en-gb/
ValueCountFrequency (%)
http://www.spaces.ch 109
 
1.4%
https://visionapartments.com/en-gb 108
 
1.4%
https://www.homestay-ag.ch/start.htm 95
 
1.2%
http://www.swiss-star.com 90
 
1.2%
https://www.visionapartments.com 82
 
1.1%
http://www.hevsg.ch/immobilienangebote 69
 
0.9%
https://projekt-interim.ch/de 57
 
0.7%
https://rtag.ch 51
 
0.7%
https://www.apartment24.ch 35
 
0.5%
https://immoseeker.ch 31
 
0.4%
Other values (5430) 6886
90.5%
2024-07-03T14:13:45.714527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 39565
 
7.9%
t 35177
 
7.0%
/ 34578
 
6.9%
c 23259
 
4.6%
a 22988
 
4.6%
h 22181
 
4.4%
i 20268
 
4.0%
w 19884
 
4.0%
m 19826
 
4.0%
s 18197
 
3.6%
Other values (72) 244955
48.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500878
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 39565
 
7.9%
t 35177
 
7.0%
/ 34578
 
6.9%
c 23259
 
4.6%
a 22988
 
4.6%
h 22181
 
4.4%
i 20268
 
4.0%
w 19884
 
4.0%
m 19826
 
4.0%
s 18197
 
3.6%
Other values (72) 244955
48.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500878
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 39565
 
7.9%
t 35177
 
7.0%
/ 34578
 
6.9%
c 23259
 
4.6%
a 22988
 
4.6%
h 22181
 
4.4%
i 20268
 
4.0%
w 19884
 
4.0%
m 19826
 
4.0%
s 18197
 
3.6%
Other values (72) 244955
48.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500878
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 39565
 
7.9%
t 35177
 
7.0%
/ 34578
 
6.9%
c 23259
 
4.6%
a 22988
 
4.6%
h 22181
 
4.4%
i 20268
 
4.0%
w 19884
 
4.0%
m 19826
 
4.0%
s 18197
 
3.6%
Other values (72) 244955
48.9%

live_viewing_url
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)33.3%
Missing27654
Missing (%)> 99.9%
Memory size216.2 KiB
https://my.matterport.com/show/?m=YRVdfY6Bvqa&ts=1

Length

Max length50
Median length50
Mean length50
Min length50

Characters and Unicode

Total characters150
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhttps://my.matterport.com/show/?m=YRVdfY6Bvqa&ts=1
2nd rowhttps://my.matterport.com/show/?m=YRVdfY6Bvqa&ts=1
3rd rowhttps://my.matterport.com/show/?m=YRVdfY6Bvqa&ts=1

Common Values

ValueCountFrequency (%)
https://my.matterport.com/show/?m=YRVdfY6Bvqa&ts=1 3
 
< 0.1%
(Missing) 27654
> 99.9%

Length

2024-07-03T14:13:45.959391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-03T14:13:46.159126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
https://my.matterport.com/show/?m=yrvdfy6bvqa&ts=1 3
100.0%

Most occurring characters

ValueCountFrequency (%)
t 18
 
12.0%
/ 12
 
8.0%
m 12
 
8.0%
s 9
 
6.0%
o 9
 
6.0%
p 6
 
4.0%
a 6
 
4.0%
h 6
 
4.0%
r 6
 
4.0%
Y 6
 
4.0%
Other values (18) 60
40.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 18
 
12.0%
/ 12
 
8.0%
m 12
 
8.0%
s 9
 
6.0%
o 9
 
6.0%
p 6
 
4.0%
a 6
 
4.0%
h 6
 
4.0%
r 6
 
4.0%
Y 6
 
4.0%
Other values (18) 60
40.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 18
 
12.0%
/ 12
 
8.0%
m 12
 
8.0%
s 9
 
6.0%
o 9
 
6.0%
p 6
 
4.0%
a 6
 
4.0%
h 6
 
4.0%
r 6
 
4.0%
Y 6
 
4.0%
Other values (18) 60
40.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 18
 
12.0%
/ 12
 
8.0%
m 12
 
8.0%
s 9
 
6.0%
o 9
 
6.0%
p 6
 
4.0%
a 6
 
4.0%
h 6
 
4.0%
r 6
 
4.0%
Y 6
 
4.0%
Other values (18) 60
40.0%

cover_image
Real number (ℝ)

MISSING 

Distinct27095
Distinct (%)100.0%
Missing562
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean8766460.8
Minimum483988
Maximum9412451
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size216.2 KiB
2024-07-03T14:13:46.368431image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum483988
5-th percentile6729723.1
Q18716366.5
median9113883
Q39307128
95-th percentile9389692.4
Maximum9412451
Range8928463
Interquartile range (IQR)590761.5

Descriptive statistics

Standard deviation1011598.2
Coefficient of variation (CV)0.11539414
Kurtosis16.984857
Mean8766460.8
Median Absolute Deviation (MAD)227426
Skewness-3.5797052
Sum2.3752725 × 1011
Variance1.0233308 × 1012
MonotonicityNot monotonic
2024-07-03T14:13:46.657146image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9277520 1
 
< 0.1%
9229736 1
 
< 0.1%
9229711 1
 
< 0.1%
9368608 1
 
< 0.1%
9229656 1
 
< 0.1%
9229627 1
 
< 0.1%
9229226 1
 
< 0.1%
9229577 1
 
< 0.1%
9229557 1
 
< 0.1%
9229539 1
 
< 0.1%
Other values (27085) 27085
97.9%
(Missing) 562
 
2.0%
ValueCountFrequency (%)
483988 1
< 0.1%
557032 1
< 0.1%
622263 1
< 0.1%
644681 1
< 0.1%
645940 1
< 0.1%
645943 1
< 0.1%
948311 1
< 0.1%
1008303 1
< 0.1%
1340576 1
< 0.1%
1340584 1
< 0.1%
ValueCountFrequency (%)
9412451 1
< 0.1%
9412442 1
< 0.1%
9412435 1
< 0.1%
9412421 1
< 0.1%
9412419 1
< 0.1%
9412389 1
< 0.1%
9412379 1
< 0.1%
9412362 1
< 0.1%
9412349 1
< 0.1%
9412331 1
< 0.1%

images
Text

Distinct27096
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size216.2 KiB
2024-07-03T14:13:47.382234image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length459
Median length324
Mean length60.569187
Min length2

Characters and Unicode

Total characters1675162
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27095 ?
Unique (%)98.0%

Sample

1st row[6168251, 6168252, 6168253, 6168254, 6168255, 6168256, 6168257, 6168258, 6168259, 6168260]
2nd row[8716673]
3rd row[8656980, 8656981, 6880004]
4th row[483988, 483989, 483990, 483991, 483992, 483993]
5th row[557032, 557031, 557033, 557035, 557036]
ValueCountFrequency (%)
562
 
0.3%
6729627 1
 
< 0.1%
6729628 1
 
< 0.1%
6716844 1
 
< 0.1%
6863470 1
 
< 0.1%
6863471 1
 
< 0.1%
6863472 1
 
< 0.1%
6863473 1
 
< 0.1%
6863474 1
 
< 0.1%
6863475 1
 
< 0.1%
Other values (185999) 185999
99.7%
2024-07-03T14:13:48.321495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 226313
13.5%
, 158913
9.5%
158913
9.5%
8 157720
9.4%
3 143027
8.5%
2 125436
7.5%
0 117254
7.0%
6 111520
6.7%
1 111457
6.7%
7 109720
6.5%
Other values (4) 254889
15.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1675162
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 226313
13.5%
, 158913
9.5%
158913
9.5%
8 157720
9.4%
3 143027
8.5%
2 125436
7.5%
0 117254
7.0%
6 111520
6.7%
1 111457
6.7%
7 109720
6.5%
Other values (4) 254889
15.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1675162
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 226313
13.5%
, 158913
9.5%
158913
9.5%
8 157720
9.4%
3 143027
8.5%
2 125436
7.5%
0 117254
7.0%
6 111520
6.7%
1 111457
6.7%
7 109720
6.5%
Other values (4) 254889
15.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1675162
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 226313
13.5%
, 158913
9.5%
158913
9.5%
8 157720
9.4%
3 143027
8.5%
2 125436
7.5%
0 117254
7.0%
6 111520
6.7%
1 111457
6.7%
7 109720
6.5%
Other values (4) 254889
15.2%
Distinct4776
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Memory size216.2 KiB
2024-07-03T14:13:48.923781image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length64
Median length2
Mean length3.10717
Min length2

Characters and Unicode

Total characters85935
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4775 ?
Unique (%)17.3%

Sample

1st row[188259]
2nd row[]
3rd row[5996]
4th row[]
5th row[]
ValueCountFrequency (%)
22882
82.0%
126021 1
 
< 0.1%
198672 1
 
< 0.1%
74089 1
 
< 0.1%
123817 1
 
< 0.1%
133960 1
 
< 0.1%
199147 1
 
< 0.1%
198443 1
 
< 0.1%
178281 1
 
< 0.1%
198786 1
 
< 0.1%
Other values (5020) 5020
 
18.0%
2024-07-03T14:13:49.706902image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
[ 27657
32.2%
] 27657
32.2%
1 5943
 
6.9%
2 5810
 
6.8%
0 3498
 
4.1%
9 2932
 
3.4%
8 2436
 
2.8%
7 1948
 
2.3%
5 1925
 
2.2%
6 1913
 
2.2%
Other values (4) 4216
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 85935
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
[ 27657
32.2%
] 27657
32.2%
1 5943
 
6.9%
2 5810
 
6.8%
0 3498
 
4.1%
9 2932
 
3.4%
8 2436
 
2.8%
7 1948
 
2.3%
5 1925
 
2.2%
6 1913
 
2.2%
Other values (4) 4216
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 85935
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
[ 27657
32.2%
] 27657
32.2%
1 5943
 
6.9%
2 5810
 
6.8%
0 3498
 
4.1%
9 2932
 
3.4%
8 2436
 
2.8%
7 1948
 
2.3%
5 1925
 
2.2%
6 1913
 
2.2%
Other values (4) 4216
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 85935
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
[ 27657
32.2%
] 27657
32.2%
1 5943
 
6.9%
2 5810
 
6.8%
0 3498
 
4.1%
9 2932
 
3.4%
8 2436
 
2.8%
7 1948
 
2.3%
5 1925
 
2.2%
6 1913
 
2.2%
Other values (4) 4216
 
4.9%

agency
Text

Distinct1381
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size216.2 KiB
2024-07-03T14:13:50.003165image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length524
Median length495
Mean length375.10872
Min length98

Characters and Unicode

Total characters10374382
Distinct characters99
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique369 ?
Unique (%)1.3%

Sample

1st row{'name': 'Schaeppi Grundstücke AG', 'name_2': None, 'street': 'Oberer Graben 3', 'zipcode': '9000', 'city': 'St. Gallen', 'country': 'CH', 'logo': {'url': '/thumb/org/2017/07/swjclw63c89d8nx0ypf8gt1bnza9y0iadiifrrebderz3narrl.png?signature=uORTnlb200xyG4N3vwHB-bXsIbPMg1_f0SYbRnednMg', 'url_org_logo_m': '/thumb/org/2017/07/swjclw63c89d8nx0ypf8gt1bnza9y0iadiifrrebderz3narrl.png?alias=org_logo_m&signature=6Jt5S-b-bBZsrmhE44UT7rAE1neO5IgenWWQlK6k8oQ'}}
2nd row{'name': 'Die Immobilien-Treuhänder', 'name_2': 'Straub & Partner AG', 'street': 'Schafisheimerstr. 14 | Postfach', 'zipcode': '5600', 'city': 'Lenzburg 1', 'country': 'CH', 'logo': {'url': '/thumb/org/2018/01/24pikyo16uxpkmtgmse7uxz3ezo5got52p0ge4dz7hregfbamh.png?signature=PPY0vl5oiFi6NdLN_YhxKxUQrHjOWmL9dfX1IRZWQkU', 'url_org_logo_m': '/thumb/org/2018/01/24pikyo16uxpkmtgmse7uxz3ezo5got52p0ge4dz7hregfbamh.png?alias=org_logo_m&signature=32xQu5HxiT5_eUzWKSUk63g0vM6LwumXa1WBqLsdVyg'}}
3rd row{'name': '', 'name_2': '', 'street': '', 'zipcode': None, 'city': '', 'country': '', 'logo': {'url': '/thumb/org/2021/09/gd6j7zww4mvw667669s2ei0h40gn6ep4o6mvs01vfu35dfexou.png?signature=TqHGaBfcrsxFik0CiREfTwYEfj4fJeSwcVZcaz7zFJA', 'url_org_logo_m': '/thumb/org/2021/09/gd6j7zww4mvw667669s2ei0h40gn6ep4o6mvs01vfu35dfexou.png?alias=org_logo_m&signature=FvIBhxWWNoadvv9eDMR6ZmoiFyhi6t-NupHXnSS_na0'}}
4th row{'name': 'Home-Vermittlung Höhener', 'name_2': None, 'street': None, 'zipcode': None, 'city': None, 'country': None, 'logo': {'url': '/thumb/org/2018/08/rocjc1px3dn5uq3gxuv1gc2rvt36pn8by6ucdcnjwhqm2bachd.png?signature=GgMsRcwkQ6iY_ilDEkWNROomwZ53FIJTt2yoCOK6mMA', 'url_org_logo_m': '/thumb/org/2018/08/rocjc1px3dn5uq3gxuv1gc2rvt36pn8by6ucdcnjwhqm2bachd.png?alias=org_logo_m&signature=-nxurwrG_2VFqfFatWNOZMjx1m0uUfLf4Xkk5HN3Hec'}}
5th row{'name': '', 'name_2': None, 'street': None, 'zipcode': None, 'city': None, 'country': None, 'logo': None}
ValueCountFrequency (%)
none 61276
 
11.5%
name 27657
 
5.2%
country 27657
 
5.2%
city 27657
 
5.2%
zipcode 27657
 
5.2%
street 27657
 
5.2%
name_2 27657
 
5.2%
logo 27657
 
5.2%
url_org_logo_m 21851
 
4.1%
url 21851
 
4.1%
Other values (3785) 232542
43.8%
2024-07-03T14:13:50.590627image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 782462
 
7.5%
503481
 
4.9%
o 469948
 
4.5%
e 433652
 
4.2%
r 369488
 
3.6%
g 352138
 
3.4%
n 345838
 
3.3%
t 310040
 
3.0%
a 299724
 
2.9%
u 279328
 
2.7%
Other values (89) 6228283
60.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10374382
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 782462
 
7.5%
503481
 
4.9%
o 469948
 
4.5%
e 433652
 
4.2%
r 369488
 
3.6%
g 352138
 
3.4%
n 345838
 
3.3%
t 310040
 
3.0%
a 299724
 
2.9%
u 279328
 
2.7%
Other values (89) 6228283
60.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10374382
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 782462
 
7.5%
503481
 
4.9%
o 469948
 
4.5%
e 433652
 
4.2%
r 369488
 
3.6%
g 352138
 
3.4%
n 345838
 
3.3%
t 310040
 
3.0%
a 299724
 
2.9%
u 279328
 
2.7%
Other values (89) 6228283
60.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10374382
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 782462
 
7.5%
503481
 
4.9%
o 469948
 
4.5%
e 433652
 
4.2%
r 369488
 
3.6%
g 352138
 
3.4%
n 345838
 
3.3%
t 310040
 
3.0%
a 299724
 
2.9%
u 279328
 
2.7%
Other values (89) 6228283
60.0%

reserved
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.1 KiB
False
27650 
True
 
7
ValueCountFrequency (%)
False 27650
> 99.9%
True 7
 
< 0.1%
2024-07-03T14:13:50.830496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Distinct15298
Distinct (%)55.3%
Missing0
Missing (%)0.0%
Memory size216.2 KiB
2024-07-03T14:13:51.307560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length90
Median length59
Mean length36.500597
Min length17

Characters and Unicode

Total characters1009497
Distinct characters102
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12180 ?
Unique (%)44.0%

Sample

1st rowRent a 127m² office in St. Margrethen SG
2nd rowRent a underground slot in Lenzburg
3rd rowRent a 119m² atelier in Dübendorf
4th rowRent a 1 room single room in St. Gallen
5th rowRent a room in a shared flat in Zürich
ValueCountFrequency (%)
a 29338
14.0%
in 29335
14.0%
rent 22072
 
10.5%
rooms 15832
 
7.6%
½ 12418
 
5.9%
apartment 11922
 
5.7%
buy 5585
 
2.7%
3 5045
 
2.4%
4 4041
 
1.9%
flat 3595
 
1.7%
Other values (3691) 70353
33.6%
2024-07-03T14:13:52.130441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
181881
18.0%
n 94338
 
9.3%
a 76341
 
7.6%
e 75573
 
7.5%
t 65118
 
6.5%
o 61269
 
6.1%
r 58575
 
5.8%
i 53119
 
5.3%
m 39627
 
3.9%
s 37190
 
3.7%
Other values (92) 266466
26.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1009497
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
181881
18.0%
n 94338
 
9.3%
a 76341
 
7.6%
e 75573
 
7.5%
t 65118
 
6.5%
o 61269
 
6.1%
r 58575
 
5.8%
i 53119
 
5.3%
m 39627
 
3.9%
s 37190
 
3.7%
Other values (92) 266466
26.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1009497
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
181881
18.0%
n 94338
 
9.3%
a 76341
 
7.6%
e 75573
 
7.5%
t 65118
 
6.5%
o 61269
 
6.1%
r 58575
 
5.8%
i 53119
 
5.3%
m 39627
 
3.9%
s 37190
 
3.7%
Other values (92) 266466
26.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1009497
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
181881
18.0%
n 94338
 
9.3%
a 76341
 
7.6%
e 75573
 
7.5%
t 65118
 
6.5%
o 61269
 
6.1%
r 58575
 
5.8%
i 53119
 
5.3%
m 39627
 
3.9%
s 37190
 
3.7%
Other values (92) 266466
26.4%

livingspace
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct928
Distinct (%)4.2%
Missing5616
Missing (%)20.3%
Infinite0
Infinite (%)0.0%
Mean142.57652
Minimum0
Maximum90000
Zeros1235
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size216.2 KiB
2024-07-03T14:13:52.394487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q154
median86
Q3128
95-th percentile335
Maximum90000
Range90000
Interquartile range (IQR)74

Descriptive statistics

Standard deviation724.20738
Coefficient of variation (CV)5.0794296
Kurtosis10846.974
Mean142.57652
Median Absolute Deviation (MAD)36
Skewness90.801785
Sum3142529
Variance524476.32
MonotonicityNot monotonic
2024-07-03T14:13:52.644119image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1235
 
4.5%
100 441
 
1.6%
70 430
 
1.6%
80 401
 
1.4%
90 372
 
1.3%
120 328
 
1.2%
75 320
 
1.2%
60 301
 
1.1%
85 282
 
1.0%
65 280
 
1.0%
Other values (918) 17651
63.8%
(Missing) 5616
 
20.3%
ValueCountFrequency (%)
0 1235
4.5%
1 17
 
0.1%
2 28
 
0.1%
3 13
 
< 0.1%
4 10
 
< 0.1%
5 6
 
< 0.1%
6 14
 
0.1%
7 17
 
0.1%
8 29
 
0.1%
9 23
 
0.1%
ValueCountFrequency (%)
90000 1
< 0.1%
23000 1
< 0.1%
18600 1
< 0.1%
18000 1
< 0.1%
11475 1
< 0.1%
11000 1
< 0.1%
10267 1
< 0.1%
8033 1
< 0.1%
8000 2
< 0.1%
6629 1
< 0.1%

Interactions

2024-07-03T14:12:55.769843image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:11:42.870004image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:11:46.690270image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-03T14:11:50.471602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-07-03T14:12:55.606482image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

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A simple visualization of nullity by column.
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Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-07-03T14:13:02.176709image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

pkslugurlshort_urlsubmit_urlstatusoffer_typeobject_categoryobject_typereferenceref_propertyref_houseref_objectalternative_referenceprice_displayprice_display_typeprice_unitrent_netrent_chargesrent_grossshort_titlepublic_titlepitch_titledescription_titledescriptionsurface_livingsurface_propertysurface_usablesurface_usable_minimumvolumespace_displaynumber_of_roomsfloorattributesis_furnishedis_temporaryis_selling_furniturestreetzipcodecitypublic_addresslatitudelongitudeyear_builtyear_renovatedmoving_date_typemoving_datevideo_urltour_urlwebsite_urllive_viewing_urlcover_imageimagesdocumentsagencyreservedrent_titlelivingspace
026706parkstr-12-9430-st-margrethen-sg/en/flat/parkstr-12-9430-st-margrethen-sg/26706//26706//en/listing/26706/submit/actRENTINDUSTRYOFFICE904250.01.401029042500140102NaNNaNNaNmonthlyNaNNaNNaN127m² officeParkstr. 12, 9430 St. Margrethen SG - by requestRent a 127m² office in St. Margrethen SGRepräsentativer Büro- oder Praxisraum von ca. 127m2Grosszügiger und Lichtdurchfluteter Büro-/ Praxisraum in unmittelbarerer Nähe zum Bahnhof St. Margrethen. \n\nAufgrund der idealen Lage, bietet der Standort auch einen zentralen Anschluss an die Autobahn sowie zahlreiche Verpflegungsmöglichkeiten. \n\n * Flexible, nutzerspezifische Raumeinteilung möglich \n * Hochwertiger Ausbaustandard \n * Deckenelemente für Lüftung und Kühlung \n * Eigene Toilette \n * Büroküche \n * Für technische Installationen "doppelter" Boden \n * Lift \n * Tiefe HK-/NK-Kosten (Minergie) \n * Parkplätze können dazu gemietet werden CHF 60.00/Mt. \n * Archivräume (ca. 8qm) können dazu gemietet werden CHF 56.00/Mt. \n * Verfügbar nach Vereinbarung \n * Mietzins auf Anfrage \n\n\n\nHaben wir Ihr Interesse geweckt so zögern Sie nicht uns für einen Besichtigungstermin zu kontaktieren.NaNNaN127.0NaNNaN127.0NaN1.0[{'name': 'petsallowed'}, {'name': 'view'}]FalseFalseFalseParkstr. 129430St. Margrethen SGParkstr. 12, 9430 St. Margrethen SG47.4523509.6358141977.0NaNagrNaNNaNNaNNaNNaN6168251.0[6168251, 6168252, 6168253, 6168254, 6168255, 6168256, 6168257, 6168258, 6168259, 6168260][188259]{'name': 'Schaeppi Grundstücke AG', 'name_2': None, 'street': 'Oberer Graben 3', 'zipcode': '9000', 'city': 'St. Gallen', 'country': 'CH', 'logo': {'url': '/thumb/org/2017/07/swjclw63c89d8nx0ypf8gt1bnza9y0iadiifrrebderz3narrl.png?signature=uORTnlb200xyG4N3vwHB-bXsIbPMg1_f0SYbRnednMg', 'url_org_logo_m': '/thumb/org/2017/07/swjclw63c89d8nx0ypf8gt1bnza9y0iadiifrrebderz3narrl.png?alias=org_logo_m&signature=6Jt5S-b-bBZsrmhE44UT7rAE1neO5IgenWWQlK6k8oQ'}}FalseRent a 127m² office in St. Margrethen SG127.0
133819martha-ringier-strasse-5600-lenzburg/en/flat/martha-ringier-strasse-5600-lenzburg/33819//33819//en/listing/33819/submit/actRENTPARKGARAGE_SLOT194..0194NaN0NaN130.0TOTALmonthly130.00.0NaNUnderground slotMartha Ringier-Strasse, 5600 Lenzburg - CHF 130 excl. utilities per monthRent a underground slot in LenzburgPARKPLATZKONTINGENT IN LENZBURGDiverse Einstellhallenparkplätze zentral in Lenzburg zu vermieten! \n \nSie benötigen kurz- oder langfristig Parkplätze? Oder Sie benötigen für ein grösseres Projekt wie einen Umbau ausreichend Parkmöglichkeiten? \n \nWir bieten Ihnen einzelne oder mehrere Parkplätze in der Einstellhalle an. Diese können ab CHF 130.00 pro Monat gemietet werden.NaNNaNNaNNaNNaNNaNNaNNaN[]FalseFalseFalseMartha Ringier-Strasse5600LenzburgMartha Ringier-Strasse, 5600 Lenzburg47.3847538.182750NaNNaNagrNaNNaNNaNNaNNaN8716673.0[8716673][]{'name': 'Die Immobilien-Treuhänder', 'name_2': 'Straub & Partner AG', 'street': 'Schafisheimerstr. 14 | Postfach', 'zipcode': '5600', 'city': 'Lenzburg 1', 'country': 'CH', 'logo': {'url': '/thumb/org/2018/01/24pikyo16uxpkmtgmse7uxz3ezo5got52p0ge4dz7hregfbamh.png?signature=PPY0vl5oiFi6NdLN_YhxKxUQrHjOWmL9dfX1IRZWQkU', 'url_org_logo_m': '/thumb/org/2018/01/24pikyo16uxpkmtgmse7uxz3ezo5got52p0ge4dz7hregfbamh.png?alias=org_logo_m&signature=32xQu5HxiT5_eUzWKSUk63g0vM6LwumXa1WBqLsdVyg'}}FalseRent a underground slot in LenzburgNaN
244676sonnentalstrasse-10-8600-dubendorf/en/flat/sonnentalstrasse-10-8600-dubendorf/44676//44676//en/listing/44676/submit/actRENTINDUSTRYATELIER71.10 (L 00 02).90017110 (L 00 02)9001NaN2325.0TOTALmonthly1980.0345.0NaN119m² atelierSonnentalstrasse 10, 8600 Dübendorf - CHF 2’325 incl. utilities per monthRent a 119m² atelier in DübendorfHochwertiges Atelier Hochbord DübendorfNach Vereinbarung vermieten wir im äusserst modernen Gebäudekomplex Hochbord dieses Atelier. \n \nDas Atelier mit insgesamt 119 m2 bietet Ihnen folgende Vorzüge: \n \n\n* Eigene Toilette\n* Bodenheizung in allen Räumen\n* Zwei einzelne Büroräume\n* Glattalbahn/ Ringwiesen in unmittelbarer Nähe\n* Bahnhof Stettbach in Gehdistanz\n* Autobahnanschluss in 3 Fahrminuten\n\n \nDieses Atelier eignet sich ausgezeichnet für verschiedene Nutzungen, als Büro, Verkaufsraum u.v.a.m. Prägen Sie das Hochbord mit Ihrem Businesskonzept - wir sind gespannt darauf. \n \nHaben wir Interesse geweckt? Dann freuen wir uns schon auf Ihre Kontaktanfrage über das Kontaktformular!NaNNaN119.0NaNNaN119.0NaNNaN[{'name': 'lift'}, {'name': 'garage'}]FalseFalseFalseSonnentalstrasse 108600DübendorfSonnentalstrasse 10, 8600 Dübendorf47.3978868.6008502018.0NaNimmNaNNaNNaNNaNNaN8656980.0[8656980, 8656981, 6880004][5996]{'name': '', 'name_2': '', 'street': '', 'zipcode': None, 'city': '', 'country': '', 'logo': {'url': '/thumb/org/2021/09/gd6j7zww4mvw667669s2ei0h40gn6ep4o6mvs01vfu35dfexou.png?signature=TqHGaBfcrsxFik0CiREfTwYEfj4fJeSwcVZcaz7zFJA', 'url_org_logo_m': '/thumb/org/2021/09/gd6j7zww4mvw667669s2ei0h40gn6ep4o6mvs01vfu35dfexou.png?alias=org_logo_m&signature=FvIBhxWWNoadvv9eDMR6ZmoiFyhi6t-NupHXnSS_na0'}}FalseRent a 119m² atelier in Dübendorf119.0
346167waldstrasse-5-9008-st-gallen/en/flat/waldstrasse-5-9008-st-gallen/46167//46167//en/listing/46167/submit/actRENTAPARTMENTSINGLE_ROOMNaNNaNNaNNaNNaN610.0TOTALmonthlyNaNNaN610.01 room single roomWaldstrasse 5, 9008 St. Gallen - CHF 610 incl. utilities per monthRent a 1 room single room in St. GallenMöbiliertes WG Zimmer zum fairen PreisIn dieser sehr schönen Wohnung vermieten wir drei möbilierte WG- Zimmer.\nDas Haus liegt im grünen aber trotzdem nur 2min von der Bushaltestelle entfernt, der direkt zur Uni und ins Zentrum fährt.17.0NaNNaNNaNNaN17.01.00.0[{'name': 'parkingspace'}, {'name': 'view'}, {'name': 'dishwasher'}]TrueFalseFalseWaldstrasse 59008St. GallenWaldstrasse 5, 9008 St. Gallen47.4424309.392510NaNNaNdat2018-09-01NaNNaNNaNNaN483988.0[483988, 483989, 483990, 483991, 483992, 483993][]{'name': 'Home-Vermittlung Höhener', 'name_2': None, 'street': None, 'zipcode': None, 'city': None, 'country': None, 'logo': {'url': '/thumb/org/2018/08/rocjc1px3dn5uq3gxuv1gc2rvt36pn8by6ucdcnjwhqm2bachd.png?signature=GgMsRcwkQ6iY_ilDEkWNROomwZ53FIJTt2yoCOK6mMA', 'url_org_logo_m': '/thumb/org/2018/08/rocjc1px3dn5uq3gxuv1gc2rvt36pn8by6ucdcnjwhqm2bachd.png?alias=org_logo_m&signature=-nxurwrG_2VFqfFatWNOZMjx1m0uUfLf4Xkk5HN3Hec'}}FalseRent a 1 room single room in St. Gallen17.0
4511598005-zurich/en/flat/8005-zurich/51159//51159//en/listing/51159/submit/actRENTSHAREDSHARED_FLATNaNNaNNaNNaNNaN1350.0TOTALmonthlyNaNNaN1350.0Room in a shared flat8005 Zürich - CHF 1’350 incl. utilities per monthRent a room in a shared flat in ZürichFurnished room with morning sunI am looking for a buoyant, easygoing person, who is looking for such a one co-habit with me (f, 50, author and therapist),my daughter (16) and our very cuddly cat. Our beautiful and very spacious 5,5 Duplex-Apartment (177m2, parquet flooring, own washing machine and dryer, 3 bathrooms, balcony and a huge roof-terrace) is situated in the new trend quarter Westside at Escherwyssplatz – very centric and still surprisingly quite. Your room: Furnished, around 18m2 and very bright (huge window, entering morning sun.18.0NaNNaNNaNNaN18.01.05.0[{'name': 'tumbler'}, {'name': 'parquetflooring'}, {'name': 'washingmachine'}, {'name': 'broadbandinternet'}, {'name': 'cable'}, {'name': 'dishwasher'}, {'name': 'lift'}, {'name': 'balconygarden'}]TrueTrueFalseNaN8005Zürich8005 Zürich47.3893038.5132002004.0NaNdat2023-04-01NaNNaNNaNNaN557032.0[557032, 557031, 557033, 557035, 557036][]{'name': '', 'name_2': None, 'street': None, 'zipcode': None, 'city': None, 'country': None, 'logo': None}FalseRent a room in a shared flat in Zürich18.0
556960loft-valangines-2000-neuchatel/en/flat/loft-valangines-2000-neuchatel/56960//56960//en/listing/56960/submit/actRENTAPARTMENTAPARTMENTNaNNaNNaNNaNNaN2370.0TOTALmonthly2100.0270.0NaN2 ½ rooms apartmentLoft, Valangines, 2000 Neuchâtel - CHF 2’370 incl. utilities per monthRent a 2 ½ rooms apartment in NeuchâtelGrand Loft unique et chaleureux, 145 m2Loft de 145 m2 comprenant un grand salon/séjour de env. 14X6,5m. (90m2) donnant sur la cuisine .\nHabitation facilement modulable qui conviendrait à personne proche de la nature, thérapeute, méditation, salle de cours, profession libérale...\nGrande cuisine entièrement agencée attenante au séjour.\nChambre à coucher (4.8x3.8m2, 18m2) plein sud, avec 2 portes, l'une donnant sur le séjour, l'autre sur un grand dressing.\nSalle d’eau (10m2) avec baignoire, douche multi-jets et vapeur, lavabo, WC, armoire de rangement.\nGrandes fenêtres panoramiques avec vue sur le lac et les Alpes. Stores électriques. \nPorte d'entrée de plein pied.\nLe hall d'entrée (ancienne réception/salle d'attente) donne avec ses 3 portes sur la cuisine, le séjour et le WC séparé, qui fait aussi office de buanderie privative avec lave-linge et sécheuse à pompe à chaleur (Energie A). \nSol chauffant en carrelage avec thermostat de contrôle dans chaque pièce, et réglage individuel des heures et jours d’enclenchement/arrêt. \nPlafonds blancs à 3.2m de hauteur. \nNombreuses prises électriques, TV/vidéo, internet, téléphone. \nPart au galetas avec monte-charge.\nEndroit avec tables, bancs et grillades dans un grand verger.\nPetit jardinet privé au pied du bâtiment.\nBon accès direct et parcage facile.\nArrêt du bus à proximité. \nAutoroute toute proche.\nForêt au bout du chemin. \nNeuchatel-ouest, région des Valangines, quartier d’habitation tranquille, belle situation arborisée et vue magnifique.\nDe préférence à non-fumeurs.\nLoyer CHF 2100.-/mois, charges CHF 270.-/mois, 1 place de parc incluse.145.0NaNNaNNaNNaN145.02.50.0[{'name': 'washingmachine'}, {'name': 'stonefloor'}, {'name': 'tumbler'}, {'name': 'cable'}, {'name': 'dishwasher'}, {'name': 'balconygarden'}, {'name': 'accessiblewithwheelchair'}, {'name': 'parkingspace'}, {'name': 'view'}]FalseFalseFalseLoft, Valangines2000NeuchâtelLoft, Valangines, 2000 Neuchâtel46.7988036.8550102004.0NaNimmNaNNaNNaNNaNNaN622263.0[622263, 622270, 622264, 622265, 622266, 622267, 622268, 622269][]{'name': 'Saco Sa', 'name_2': None, 'street': None, 'zipcode': None, 'city': None, 'country': None, 'logo': None}FalseRent a 2 ½ rooms apartment in Neuchâtel145.0
658714bahnhofstrasse-2527-8280-kreuzlingen/en/flat/bahnhofstrasse-2527-8280-kreuzlingen/58714//58714//en/listing/58714/submit/actRENTINDUSTRYPARKING_SURFACEBahnhofstrasse..Garage.308168.b2ac31f4-d78d-11e8-bb6c-a4bf01195aaaBahnhofstrasse..Garage308168b2ac31f4-d78d-11e8-bb6c-a4bf01195aaaNaN110.0TOTALmonthlyNaNNaN110.0Parking surfaceBahnhofstrasse 25/27, 8280 Kreuzlingen - CHF 110 incl. utilities per monthRent a parking surface in KreuzlingenReinschauen lohnt sich...!Wir vermieten ab sofort an der Bahnhofstrasse 25/27, 8280 Kreuzlingen die letzten Garagen für Fr. 110.- / Monat. \n \nVerpassen Sie diese Gelegenheit nicht und kontaktieren Sie uns noch heute für einen unverbindlichen Besichtigungstermin! \nWir freuen uns auf Ihre Kontaktaufnahme! \nLiving Group \ninfo@livinggroup.ch \nTel. 043 960 73 80NaNNaNNaNNaNNaNNaNNaNNaN[{'name': 'parkingspace'}]FalseFalseFalseBahnhofstrasse 25/278280KreuzlingenBahnhofstrasse 25/27, 8280 Kreuzlingen47.6519839.170387NaNNaNimmNaNNaNNaNNaNNaN644681.0[644681][]{'name': 'Living Group', 'name_2': None, 'street': 'Postfach', 'zipcode': '8036', 'city': 'Zürich', 'country': 'CH', 'logo': {'url': '/thumb/org/2018/12/se8q30pcm06kh94w3cgmpe5c4eg64x4h3zrhh57c811ov0i50j.jpg?signature=-Pl1tcTxedUZRrUhfD-s4EmQHe_p0M6zZ1fuTOznqV0', 'url_org_logo_m': '/thumb/org/2018/12/se8q30pcm06kh94w3cgmpe5c4eg64x4h3zrhh57c811ov0i50j.jpg?alias=org_logo_m&signature=lqgPHwXU8VIAA6qgmrqeNQXEUeL3e2QyAy5D7HzRCP8'}}FalseRent a parking surface in KreuzlingenNaN
758833kirchweg-2127-4208-nunningen/en/flat/kirchweg-2127-4208-nunningen/58833//58833//en/listing/58833/submit/actRENTPARKOPEN_SLOTKirchweg..PPL.308154.abdd07cf-d78d-11e8-bb6c-a4bf01195aaaKirchweg..PPL308154abdd07cf-d78d-11e8-bb6c-a4bf01195aaaNaN40.0TOTALmonthlyNaNNaN40.0Open slotKirchweg 21/27, 4208 Nunningen - CHF 40 incl. utilities per monthRent a open slot in NunningenLetzte Parkplätze noch zu vermieten!Wir vermieten am Kirchweg 21-27, Nunningen die letzten Parkplätze für Fr. 40.-/Monat. \nWir freuen uns auf Ihre Kontaktaufnahme! \nLiving Group \n043 960 73 80 \ninfo@livinggroup.chNaNNaNNaNNaNNaNNaNNaNNaN[{'name': 'parkingspace'}]FalseFalseFalseKirchweg 21/274208NunningenKirchweg 21/27, 4208 Nunningen47.3935007.614562NaNNaNimmNaNNaNNaNNaNNaN645940.0[645940][]{'name': 'Living Group', 'name_2': None, 'street': 'Postfach', 'zipcode': '8036', 'city': 'Zürich', 'country': 'CH', 'logo': {'url': '/thumb/org/2018/12/se8q30pcm06kh94w3cgmpe5c4eg64x4h3zrhh57c811ov0i50j.jpg?signature=-Pl1tcTxedUZRrUhfD-s4EmQHe_p0M6zZ1fuTOznqV0', 'url_org_logo_m': '/thumb/org/2018/12/se8q30pcm06kh94w3cgmpe5c4eg64x4h3zrhh57c811ov0i50j.jpg?alias=org_logo_m&signature=lqgPHwXU8VIAA6qgmrqeNQXEUeL3e2QyAy5D7HzRCP8'}}FalseRent a open slot in NunningenNaN
858837rankwog-11-4632-trimbach/en/flat/rankwog-11-4632-trimbach/58837//58837//en/listing/58837/submit/actRENTINDUSTRYPARKING_SURFACERankwog11..Tiefgarage.308164.b04a6fe9-d78d-11e8-bb6c-a4bf01195aaaRankwog11..Tiefgarage308164b04a6fe9-d78d-11e8-bb6c-a4bf01195aaaNaN95.0TOTALmonthlyNaNNaN95.0Parking surfaceRankwog 11, 4632 Trimbach - CHF 95 incl. utilities per monthRent a parking surface in TrimbachReinschauen lohnt sich...! Einstellplätze per sofort!Wir vermieten per sofort Einstellplätze in der Tiefgarage Rankwog 11, 4632 Trimbach. \n \nSind Sie interessiert? \nWir freuen uns auf Ihre Kontaktaufnahme! \nLiving Group \ninfo@livinggroup.ch \n043 960 73 80NaNNaNNaNNaNNaNNaNNaN0.0[{'name': 'parkingspace'}]FalseFalseFalseRankwog 114632TrimbachRankwog 11, 4632 Trimbach47.3701607.914666NaNNaNimmNaNNaNNaNNaNNaN645943.0[645943][]{'name': 'Living Group', 'name_2': None, 'street': 'Postfach', 'zipcode': '8036', 'city': 'Zürich', 'country': 'CH', 'logo': {'url': '/thumb/org/2018/12/se8q30pcm06kh94w3cgmpe5c4eg64x4h3zrhh57c811ov0i50j.jpg?signature=-Pl1tcTxedUZRrUhfD-s4EmQHe_p0M6zZ1fuTOznqV0', 'url_org_logo_m': '/thumb/org/2018/12/se8q30pcm06kh94w3cgmpe5c4eg64x4h3zrhh57c811ov0i50j.jpg?alias=org_logo_m&signature=lqgPHwXU8VIAA6qgmrqeNQXEUeL3e2QyAy5D7HzRCP8'}}FalseRent a parking surface in TrimbachNaN
962870bodenweg-2-4226-breitenbach/en/flat/bodenweg-2-4226-breitenbach/62870//62870//en/listing/62870/submit/actRENTPARKGARAGE_SLOT.332476.b219b3e1-14b9-11e9-8279-a4bf01195aaaNaN332476b219b3e1-14b9-11e9-8279-a4bf01195aaaNaN95.0TOTALmonthlyNaNNaN95.0Underground slotBodenweg 2, 4226 Breitenbach - CHF 95 incl. utilities per monthRent a underground slot in BreitenbachLetzte Einstellplätze in der Tiefgarage**Einstellplätze zu vermieten** \nWir vermieten per sofort die letzten Einstellplätze! \n \nSind Sie interessiert? \n \nWir freuen uns auf Ihre Kontaktaufnahme! \n \n \n \nLiving Group \n \ninfo@livinggroup.ch \n \n043 960 73 80NaNNaNNaNNaNNaNNaNNaNNaN[]FalseFalseFalseBodenweg 24226BreitenbachBodenweg 2, 4226 Breitenbach47.4022917.544379NaNNaNimmNaNNaNNaNNaNNaNNaN[][]{'name': 'Living Group', 'name_2': None, 'street': 'Postfach', 'zipcode': '8036', 'city': 'Zürich', 'country': 'CH', 'logo': {'url': '/thumb/org/2018/12/se8q30pcm06kh94w3cgmpe5c4eg64x4h3zrhh57c811ov0i50j.jpg?signature=-Pl1tcTxedUZRrUhfD-s4EmQHe_p0M6zZ1fuTOznqV0', 'url_org_logo_m': '/thumb/org/2018/12/se8q30pcm06kh94w3cgmpe5c4eg64x4h3zrhh57c811ov0i50j.jpg?alias=org_logo_m&signature=lqgPHwXU8VIAA6qgmrqeNQXEUeL3e2QyAy5D7HzRCP8'}}FalseRent a underground slot in BreitenbachNaN
pkslugurlshort_urlsubmit_urlstatusoffer_typeobject_categoryobject_typereferenceref_propertyref_houseref_objectalternative_referenceprice_displayprice_display_typeprice_unitrent_netrent_chargesrent_grossshort_titlepublic_titlepitch_titledescription_titledescriptionsurface_livingsurface_propertysurface_usablesurface_usable_minimumvolumespace_displaynumber_of_roomsfloorattributesis_furnishedis_temporaryis_selling_furniturestreetzipcodecitypublic_addresslatitudelongitudeyear_builtyear_renovatedmoving_date_typemoving_datevideo_urltour_urlwebsite_urllive_viewing_urlcover_imageimagesdocumentsagencyreservedrent_titlelivingspace
276471254463goldbrunnenstrasse-144-8055-zurich/en/flat/goldbrunnenstrasse-144-8055-zurich/1254463//1254463//en/listing/1254463/submit/actRENTSHAREDSHARED_FLATNaNNaNNaNNaNNaN1100.0TOTALmonthlyNaNNaN1100.0Room in a shared flatGoldbrunnenstrasse 144, 8055 Zürich - CHF 1’100 incl. utilities per monthRent a room in a shared flat in ZürichRenting my Room**Möbliertes Zimmer für ca 5-6 Monate zur Untervermietung** \nHallo zäme, \nIch möchte gerne mein Zimmer in unserer charmanten 2-Personen-WG für ca. 5 Monate untervermieten. Meine Mitbewohnerin wird ebenfalls von ca. Mitte September bis Anfang Dezember weg sein. \n**Lage:** \nZürich Wiedikon, am Goldbrunnenplatz, Lage mit exzellenter Anbindung an öffentliche Verkehrsmittel (Tram, Bus). Einkaufsmöglichkeiten gleich in der Nähe und si wie viele tolle Resturants, Kaffees und Bars. \n**Details:** \n\n* **Größe:** ca. 13m²\n* **Miete:** 1100/- pro Monat (inkl. Nebenkosten) + 22.50.- Internet\n* **Zeitraum:** 01.09.2024 - 31.01.2025 (5 Monate) genaue Einzugs- und Auszugsdaten bin ich flexibel\n\n**Ausstattung:** \n\n* Voll möbliert (Bett, Kleiderschrank, Kommode)\n* Helles, freundliches Zimmer mit großem Fenster\n* High-Speed-Internet (WLAN)\n* Nutzung der voll ausgestatteten Küche (Kühlschrank, Herd, Ofen, Mikrowelle, Geschirrspüler)\n* Modernes Badezimmer zur Mitbenutzung\n* Ganz gemütliches Wohnzimmer mit Balkon auch zur Mitbenutzung\n* Geteilte Waschküche im Keller (fix jeder Do Waschtag)\n\n \n**Furnished room for rent for approx. 5-6 months** \nHello everyone, \n \nI would like to sublet my room in our charming 2-person flat share for approx. 5 months. My flatmate will also be away from around mid-September to the beginning of December. \n \nLocation: \nZurich Wiedikon, just off Goldbrunnenplatz with excellent public transport connections (tram, bus). Shopping facilities nearby and many great restaurants, cafes and bars within a few minutes walk. \n \n**Details:** \n· Size: approx. 13m² \n· Rent: 1100/- per month (incl. additional costs) \n· Period: 01.09.2024 - 31.01.2025 (5 months) Entry and exit I am still a little flexible you could still see what fits. \n \n**Equipment:** \n· Fully furnished (bed, wardrobe, drawers) \n· Bright, friendly room with large window \n· High-speed Internet (WLAN) \n· Use of the fully equipped kitchen (fridge, cooker, oven, microwave) \n· Modern bathroom for shared use \n· Very cosy living room with balcony also for shared useNaNNaNNaNNaNNaNNaN1.02.0[{'name': 'balconygarden'}, {'name': 'lift'}, {'name': 'parquetflooring'}]FalseTrueFalseGoldbrunnenstrasse 1448055ZürichGoldbrunnenstrasse 144, 8055 Zürich47.3718928.511351NaNNaNdat2024-09-01NaNNaNNaNNaN9411054.0[9411054, 9411056, 9411033, 9411051, 9411012, 9411053][]{'name': None, 'name_2': None, 'street': None, 'zipcode': None, 'city': None, 'country': None, 'logo': None}FalseRent a room in a shared flat in ZürichNaN
276481254467effretikerstrasse-2a-8604-volketswil/en/flat/effretikerstrasse-2a-8604-volketswil/1254467//1254467//en/listing/1254467/submit/actRENTPARKGARAGE_SLOTNaNNaNNaNNaNNaN130.0TOTALmonthlyNaNNaN130.0Underground slotEffretikerstrasse 2A, 8604 Volketswil - CHF 130 incl. utilities per monthRent a underground slot in VolketswilTiefgaragenparkplatz in VolketswilTiefgaragenplatz in Kindhausen per sofort verfügbar. Parkplatz Nummer 5 auf den Bildern. Der Inserent ist Mieter, Untermietvertrag würde nach Rücksprache mit der Verwaltung aufgesetzt. Stehe sehr gerne für Fragen oder Besichtigungen zur Verfügung.NaNNaNNaNNaNNaNNaNNaNNaN[{'name': 'parkingspace'}, {'name': 'garage'}]FalseFalseFalseEffretikerstrasse 2A8604VolketswilEffretikerstrasse 2A, 8604 Volketswil47.4061228.681111NaNNaNimmNaNNaNNaNNaNNaN9412379.0[9412379, 9412380, 9412378][]{'name': None, 'name_2': None, 'street': None, 'zipcode': None, 'city': None, 'country': None, 'logo': None}FalseRent a underground slot in VolketswilNaN
2764912544688004-zurich/en/flat/8004-zurich/1254468//1254468//en/listing/1254468/submit/actRENTSHAREDSHARED_FLATNaNNaNNaNNaNNaN1137.0TOTALmonthlyNaNNaN1137.0Room in a shared flat8004 Zürich - CHF 1’137 incl. utilities per monthRent a room in a shared flat in ZürichZimmer 1. StockEi**nmalig schönes Zimmer in Altbauwohnung Nähe Helvetiaplatz** \n \nPer Sofort (oder nach Vereinbarung) wird dieses hübsche Zimmer frei. Die Wohnung ist ca. 105m2 gross – ein wunderschön renovierter Altbau am oberen, ruhigeren Teil der Langstrasse. 4,5 Zimmer, Küche mit Gaskochherd & Geschirrspüler, Balkon zum Innenhof, Bad mit Badewanne & Waschmaschine/Tumbler, grosses Wohnzimmer, ganze Wohnung mit Stuckaturen an der Decke und mit schönen, alten Böden. Tramlinien 2, 3, 8 sowie Busstation Kernstrasse praktisch vor der Haustür. \n \nDas freie Zimmer ist mit Abstand das tollste der ganzen Wohnung: es geht als einziges bei 2 Türen hinaus, hat ein Fenster, \nZusammenleben würdest du mit Michael (35, Standortleiter in einer Gelateria (yes; Ice Cream for freee!) und Santiago (33, Koch)… Wir sitzen gern mal abends bei einem Vino draussen auf dem Balkon zusammen und quatschen über Gott und die Welt – ansonsten lebt jeder sein eigenes Leben und wir geben einander viel Freiraum. \nWir lieben die zentrale Lage unserer Wohnung mit Cafés, Kinos, Bars, Yogastudios, Bibliotheken, Märkten etc. in Gehdistanz. Unser Zuhause ist jedoch ein Rückzugsort, hier soll sich jeder entspannen und wohlfühlen können. \nWir suchen nach einer ruhigen, aufgeschlossenen Person über 30 und mit Festanstellung, die es schätzt, zentral zu leben und sich Zuhause dennoch zurückziehen zu können. Respekt und gegenseitige Rücksichtnahme sind Key :)… \nSounds good? Dann freuen wir uns sehr darauf, wenn du uns ein paar Zeilen zu dir schreibst (und wieso du zu uns passen könntest) und wir dich allenfalls schon bald zur Besichtigung einladen dürfen. \nWICHTIG: \n \n\- Bitte melde dich nur, wenn du über 30 bist und eine Festanstellung hast.105.0NaNNaNNaNNaN105.01.01.0[{'name': 'balconygarden'}, {'name': 'lift'}, {'name': 'dishwasher'}, {'name': 'parquetflooring'}, {'name': 'tumbler'}, {'name': 'broadbandinternet'}, {'name': 'washingmachine'}]FalseFalseFalseNaN8004Zürich8004 Zürich47.3808028.5173211850.02010.0immNaNNaNNaNNaNNaN9412389.0[9412389, 9412392, 9412391, 9412386, 9412382, 9412388, 9412385, 9412384, 9412387, 9412390, 9412383][]{'name': None, 'name_2': None, 'street': None, 'zipcode': None, 'city': None, 'country': None, 'logo': None}FalseRent a room in a shared flat in Zürich105.0
276501254471buckhauserstrasse-29-8048-zurich/en/flat/buckhauserstrasse-29-8048-zurich/1254471//1254471//en/listing/1254471/submit/actRENTAPARTMENTAPARTMENTNaNNaNNaNNaNNaN3270.0TOTALmonthlyNaNNaN3270.03 ½ rooms apartmentBuckhauserstrasse 29, 8048 Zurich - CHF 3’270 incl. utilities per monthRent a 3 ½ rooms apartment in ZurichRent a 3 ½ rooms apartment in ZurichFurnished 3.5 room apartment \nWe offer you an attractive 3.5 room apartment in a quiet location. The apartment has 2 bedrooms. A spacious balcony invites you to relax and enjoy the surroundings. \nThe apartment is characterized by excellent access to the city, as the Tram line 2 stop is located right next door. \nFeatures: \n\n* 3.5 room apartment with 102m2\n* 2x bedrooms/office\n* balcony\n* 2x bathrooms\n* washing and tumbler in the apartment\n* cellar compartment available\n\nFurnishing: \n\n* kitchen furniture\n* Sofa incl. table\n* sideboard\n* lamps\n* dining table + chairs\n* bookshelf\n* 1 bed (160cm)\n* if necessary second bed can be provided\n* 2 closets (Ikea PAX)\n* lounge balcony\n* desk\n\nLocation: \nThe apartment is located in a central location in Zurich. Within walking distance you will find shopping facilities such as Coop, Denner, Aldi and Migros, which cover your daily needs. \nRental period: \nThe apartment is available from September 1st and is initially rented for a period of one year. However, there is a possibility of extension and/or taking over the main contract. \n \nRental conditions: \nRent: CHF 3'270 per month \nDeposit: 2 months rent102.0NaNNaNNaNNaN102.03.52.0[{'name': 'balconygarden'}, {'name': 'lift'}, {'name': 'garage'}, {'name': 'parkingspace'}, {'name': 'accessiblewithwheelchair'}, {'name': 'view'}, {'name': 'parquetflooring'}, {'name': 'washingmachine'}, {'name': 'stonefloor'}]TrueFalseFalseBuckhauserstrasse 298048ZurichBuckhauserstrasse 29, 8048 Zurich47.3848728.4936512018.0NaNdat2024-09-01NaNNaNNaNNaN9412325.0[9412325, 9412324, 9412328, 9412327, 9412316, 9412330, 9412319, 9412318, 9412321, 9412322, 9412323, 9412326, 9412329, 9412317][]{'name': None, 'name_2': None, 'street': None, 'zipcode': None, 'city': None, 'country': None, 'logo': None}FalseRent a 3 ½ rooms apartment in Zurich102.0
276511254474splugenstrasse-8-8002-zurich/en/flat/splugenstrasse-8-8002-zurich/1254474//1254474//en/listing/1254474/submit/actRENTSHAREDSHARED_FLATNaNNaNNaNNaNNaN1390.0TOTALmonthlyNaNNaN1390.0Room in a shared flatSplügenstrasse 8, 8002 Zürich - CHF 1’390 incl. utilities per monthRent a room in a shared flat in Zürich5 Zimmer WG im Herzen von Zürich direkt am See!Hallo zusammen und herzlich willkommen bei der NextGen Properties! 🎯 \n \nPer sofort vermieten wir zwei Zimmer in dieser hellen 5-Zimmer-Wohnung an bester Lage direkt am See (8002 Enge). \n \n**Hier sind die Details:** \n \n\n* Mietbeginn: Per sofort, 01.07.2024 (spätestens 16.07.)\n* Miete: 1'340 CHF pro Monat (inkl. Nebenkosten)\n* Zimmergröße: Entweder 13m'2 mit Balkon oder 17m'2 ohne Balkon (Preis ist der gleiche)\n* Gemeinschaftseinrichtungen: 2 x Balkon zum Entspannen (einen für die Morgensonne und einen für Abends), grosse Küche und 2 Bäder, damit alle Bewohner morgens genug Platz haben\n\n**Wichtig:** \nAktuell ist die WG mit 3 Personen gefüllt (1 x weiblich, anfangs 20 und 2 x männlich, Ende 20). Für die letzten beiden Zimmer suchen wir also tendenziell Frauen. \n \n**Möblierung:** \nSolltest du dein Zimmer möbliert wünschen, machen wir das gerne für dich. Die Möblierung würde einmalig 750 CHF kosten. \n \n**Und nun zu dir und was du erfüllen solltest:** \n\n* Bist vorzugsweise weiblich\n* Bist zwischen 18 und 30 Jahren alt\n* ⁠bist selbst Student oder bereits im Arbeitsleben angekommen\n* Nicht-Raucher 🚭\n* Hast, wenn du aus dem Ausland kommst, mind. die Aufenthaltsbewilligung B\n\n**Zu uns:** \n\n* Wir, die NextGen Properties 🎯, sind ein Co-Living Anbieter und bieten so in gesamthaft 7 Schweizer Städten WGs und Co-Livings an. 🏘️\n* In unseren bereits über 80 WGs leben mittlerweile über 200 junge Menschen - darunter Studierende, Lehrlinge aber auch Doktoranden sowie bereits arbeitstätige junge Menschen! 👨🏼‍🎓\n* Unser Ziel und unsere Mission besteht darin, einfacheren und schnelleren Zugang zu flexiblen Wohnmöglichkeiten zu offerieren und somit sicherzustellen, dass schweizweit auch wirklich jeder, welcher ein WG Zimmer sucht, auch eines finden wird! 😊\n\n \nSolltest du mehr zu uns erfahren wollen, findest du uns ansonsten auch auf Instagram (https://www.instagram.com/next.genproperties/) oder natürlich auf unserer Webseite (www.nextgenproperties.ch). \n \nWir freuen uns aufjedenfall auf deine Bewerbung und wünschen dir - falls du dich für uns entscheiden solltest - bereits jetzt ein ganz gutes Einleben in deinem neuen Zuhause und heissen dich in der NextGen Community herzlich willkommen! 😊 \n \nDein NextGen Team 🎯NaNNaNNaNNaNNaNNaN1.0NaN[]FalseFalseFalseSplügenstrasse 88002ZürichSplügenstrasse 8, 8002 Zürich47.3644728.533621NaNNaNimmNaNNaNNaNhttps://www.nextgenproperties.ch/NaN9412421.0[9412421, 9412422, 9412423, 9412424, 9412425, 9412426, 9412427, 9412428, 9412429, 9412430, 9412431, 9412432, 9412433, 9412434][]{'name': 'NextGen Properties', 'name_2': '', 'street': 'Via Suvretta 6', 'zipcode': '7500', 'city': 'St. Moritz', 'country': 'CH', 'logo': {'url': '/thumb/org/2024/06/xvho2l6wl3n0bka260qqodnj9c1spwr0hq05jft899jxv4den1.png?signature=R56JoMuWCz0tOYqitgYjz6PhRzbwSl-O9gS31sgVNRI', 'url_org_logo_m': '/thumb/org/2024/06/xvho2l6wl3n0bka260qqodnj9c1spwr0hq05jft899jxv4den1.png?alias=org_logo_m&signature=qO5-m3IfknB6MY8AryvhKsK7zBmn21bsQvp6jI33-uA'}}FalseRent a room in a shared flat in ZürichNaN
276521254475pflugstr-10-8006-zurich/en/flat/pflugstr-10-8006-zurich/1254475//1254475//en/listing/1254475/submit/actRENTSHAREDSHARED_FLATNaNNaNNaNNaNNaN1290.0TOTALmonthly1190.0100.0NaNRoom in a shared flatPflugstr. 10, 8006 Zürich - CHF 1’290 incl. utilities per monthRent a room in a shared flat in ZürichCosy room in Kreis 6Ein Zimmer in einer 3er WG in kreis 6. \nDas Zimmer und die Wohnung ist komplett möbliert. \nRuhige und sehr zentrale Lage mitten in Zürich.16.0NaNNaNNaNNaN16.01.01.0[{'name': 'raisedgroundfloor'}, {'name': 'parquetflooring'}, {'name': 'washingmachine'}, {'name': 'tumbler'}, {'name': 'broadbandinternet'}, {'name': 'dishwasher'}, {'name': 'lift'}, {'name': 'balconygarden'}]TrueFalseFalsePflugstr. 108006ZürichPflugstr. 10, 8006 Zürich47.3899728.5375911930.02019.0immNaNNaNNaNNaNNaN9412435.0[9412435, 9412436, 9412437, 9412438, 9412439, 9412440, 9412441][]{'name': None, 'name_2': None, 'street': None, 'zipcode': None, 'city': None, 'country': None, 'logo': None}FalseRent a room in a shared flat in Zürich16.0
276531254477schwimmbadstrasse-7-8302-kloten/en/flat/schwimmbadstrasse-7-8302-kloten/1254477//1254477//en/listing/1254477/submit/actRENTAPARTMENTAPARTMENTNaNNaNNaNNaNNaN2500.0TOTALmonthly2200.0300.0NaN4 rooms apartmentSchwimmbadstrasse 7, 8302 Kloten - CHF 2’500 incl. utilities per monthRent a 4 rooms apartment in Kloten"Komplett sanierte 4-Zimmer Wohnung"An sehr gut erschlossener Lage vermieten wir ab Oktober 2024 eine komplett renovierte Wohnung mit: \n\n* neue moderne Küche\n* neues Bad\n* Wandschränke im Korridor\n* Hochwertigen Bodenbelägen\n* Kellerabteil\n* Winde (Estrichabteil)\n* Balkon zum Verweilen\n* In Fussweite zur Bushaltestelle oder Bahnhof Kloten (5 Min.)\n\nEinen Garagenparkplatz kann für 150.00 CHF / Monat dazu gemietet werden. \n \nÜberzeugen Sie sich selbst und buchen Sie noch heute einen Besichtigungstermin. \n \nWir freuen uns auf Sie!74.0NaNNaNNaNNaN74.04.01.0[{'name': 'balconygarden'}, {'name': 'garage'}, {'name': 'lift'}, {'name': 'dishwasher'}, {'name': 'cable'}, {'name': 'parquetflooring'}, {'name': 'petsallowed'}]FalseFalseFalseSchwimmbadstrasse 78302KlotenSchwimmbadstrasse 7, 8302 Kloten47.4448728.5811411960.02023.0dat2024-10-01NaNNaNNaNNaN9412419.0[9412419, 9412416, 9412414, 9412415, 9412417, 9412418, 9412420, 9412413][]{'name': 'Maretimo AG', 'name_2': '', 'street': '', 'zipcode': '', 'city': '', 'country': 'CH', 'logo': {'url': '/thumb/org/2022/02/sfz8mqroc6arreoup3j7k4bj7wzuxkm7jqbgzizbbxkb85vfca.png?signature=-D0qaqsYbs_zgo8Pt_b2D4gWK8eIlIfp4kr8koe1vs8', 'url_org_logo_m': '/thumb/org/2022/02/sfz8mqroc6arreoup3j7k4bj7wzuxkm7jqbgzizbbxkb85vfca.png?alias=org_logo_m&signature=H12iU0UX4UR5k9g14TGirDCrID4enoYiB9HCHVYdkMU'}}FalseRent a 4 rooms apartment in Kloten74.0
276541254479j-h-prstalozzialee-13-2503-biel-bienne/en/flat/j-h-prstalozzialee-13-2503-biel-bienne/1254479//1254479//en/listing/1254479/submit/actRENTSHAREDSHARED_FLATNaNNaNNaNNaNNaN600.0TOTALmonthlyNaNNaN600.0Room in a shared flatJ. H. Prstalozzialee 13, 2503 Biel Bienne - CHF 600 incl. utilities per monthRent a room in a shared flat in Biel BienneFrei stehendes Haus ( 3 Zimmer) grosser Garten an ruhiger Lage , ÖV Bus Nr. 3 vor der TüreParkplatz inkl.60.0NaN60.050.0NaN60.01.00.0[{'name': 'parkingspace'}, {'name': 'balconygarden'}, {'name': 'broadbandinternet'}, {'name': 'cable'}, {'name': 'washingmachine'}, {'name': 'dishwasher'}, {'name': 'parquetflooring'}, {'name': 'raisedgroundfloor'}, {'name': 'ramp'}, {'name': 'view'}, {'name': 'petsallowed'}]TrueFalseFalseJ. H. Prstalozzialee 132503Biel BienneJ. H. Prstalozzialee 13, 2503 Biel Bienne47.1289427.2605411942.02008.0agrNaNNaNNaNNaNNaN9412442.0[9412442, 9412443, 9412444, 9412445, 9412446, 9412447, 9412448, 9412449][]{'name': None, 'name_2': None, 'street': None, 'zipcode': None, 'city': None, 'country': None, 'logo': None}FalseRent a room in a shared flat in Biel Bienne60.0
276551254483sonnhalde-34a-3065-bolligen/en/flat/sonnhalde-34a-3065-bolligen/1254483//1254483//en/listing/1254483/submit/actRENTAPARTMENTAPARTMENTNaNNaNNaNNaNNaN1125.0TOTALmonthly975.0150.0NaN1 room apartmentSonnhalde 34a, 3065 Bolligen - CHF 1’125 incl. utilities per monthRent a 1 room apartment in BolligenStudiowohnung in Bolligen mit Terrassen und AlpenblickIhr neues Zuhause erwartet Sie: ein lichtdurchflutetes Studio (27.5 m^2) im Neubau mit Terrasse, Dachterrasse und atemberaubendem Bergblick. Optional steht eine Garage zur Verfügung gegen einen Aufpreis von 150.-/Monat. Die ruhige Lage bietet kurze Wege zu Einkaufsmöglichkeiten und dem Bahnhof Ittigen. In nur 8 Minuten erreichen Sie mit dem Zug das Zentrum von Bern. Geniessen Sie die Natur in der Nähe mit Spazierwegen zum Mannenberg (15 Gehminuten) und Schermenwald (10 Gehminuten). Ein zusätzlicher Nebenraum steht als Keller zur Verfügung. \nDie Nebenkosten sind nicht inklusive, jedoch wird durch die zwei Solarpaneele auf dem Dach Energie gespart. Die Nebenkosten betragen monatlich 150 Franken. Am Ende des Jahres wird eine detaillierte Abrechnung aller Betriebskosten gemäss Mietvertrag erstellt. \nDer Erstbezug ist frühestens ab August 2024 möglich.27.0NaN35.0NaNNaN27.01.01.0[{'name': 'view'}, {'name': 'balconygarden'}, {'name': 'garage'}, {'name': 'parkingspace'}, {'name': 'dishwasher'}, {'name': 'broadbandinternet'}, {'name': 'washingmachine'}]FalseFalseFalseSonnhalde 34a3065BolligenSonnhalde 34a, 3065 Bolligen46.9736627.4893312024.0NaNagrNaNNaNNaNNaNNaN9412451.0[9412451, 9412452, 9412453, 9412454, 9412456, 9412460, 9412461, 9412462, 9412463][]{'name': None, 'name_2': None, 'street': None, 'zipcode': None, 'city': None, 'country': None, 'logo': None}FalseRent a 1 room apartment in Bolligen27.0
276561254486bahnhofplatz-13-8953-dietikon/en/flat/bahnhofplatz-13-8953-dietikon/1254486//1254486//en/listing/1254486/submit/actRENTAPARTMENTAPARTMENTNaNNaNNaNNaNNaN2050.0TOTALmonthlyNaNNaN2050.03 ½ rooms apartmentBahnhofplatz 13, 8953 Dietikon - CHF 2’050 incl. utilities per monthRent a 3 ½ rooms apartment in Dietikon3.5 Zimmer wohnung zu vermietenDirekt am Bahnhof von Dietikon suchen wir einen Nachmieter für unsere schöne und grosszügige Wohnung mit Terasse.\n\nInnerhalb von wenigen Gehminuten erreichen Sie:\n\n° den Bahnhof\n° jegliche Einkaufsmöglichkeiten\n° Restaurants für jeden Geschmack\n° Und vieles mehr !92.0NaN92.092.0NaN92.03.52.0[{'name': 'balconygarden'}, {'name': 'garage'}, {'name': 'lift'}, {'name': 'accessiblewithwheelchair'}, {'name': 'dishwasher'}, {'name': 'cable'}, {'name': 'petsallowed'}, {'name': 'parquetflooring'}, {'name': 'view'}]FalseFalseFalseBahnhofplatz 138953DietikonBahnhofplatz 13, 8953 Dietikon47.4058128.403451NaNNaNdat2024-09-01NaNNaNNaNNaN9412184.0[9412184, 9412179, 9412181, 9412178, 9412182, 9412183, 9412180, 9412185, 9412176, 9412177, 9412186, 9412187][]{'name': None, 'name_2': None, 'street': None, 'zipcode': None, 'city': None, 'country': None, 'logo': None}FalseRent a 3 ½ rooms apartment in Dietikon92.0